From 7a52baac43a672acc3828ffe9dada3246413a41c Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Thu, 27 May 2021 17:13:39 -0700 Subject: [PATCH 01/15] Add paper --- papers/jyotika_singh/comments.png | Bin 0 -> 101002 bytes papers/jyotika_singh/comments_2020.png | Bin 0 -> 131847 bytes papers/jyotika_singh/comments_month.png | Bin 0 -> 24519 bytes papers/jyotika_singh/comments_year.png | Bin 0 -> 20202 bytes papers/jyotika_singh/figure1.png | Bin 0 -> 18932 bytes papers/jyotika_singh/flights.png | Bin 0 -> 120806 bytes papers/jyotika_singh/hotels.png | Bin 0 -> 40286 bytes papers/jyotika_singh/jyotika_singh.rst | 463 ++++++++++++++++++++++++ papers/jyotika_singh/likes_month.png | Bin 0 -> 26651 bytes papers/jyotika_singh/likes_year.png | Bin 0 -> 23018 bytes papers/jyotika_singh/locs.png | Bin 0 -> 206139 bytes papers/jyotika_singh/stats_shift.png | Bin 0 -> 97600 bytes papers/jyotika_singh/videos.png | Bin 0 -> 207815 bytes papers/jyotika_singh/views_month.png | Bin 0 -> 29568 bytes papers/jyotika_singh/views_year.png | Bin 0 -> 24326 bytes 15 files changed, 463 insertions(+) create mode 100644 papers/jyotika_singh/comments.png create mode 100644 papers/jyotika_singh/comments_2020.png create mode 100644 papers/jyotika_singh/comments_month.png create mode 100644 papers/jyotika_singh/comments_year.png create mode 100644 papers/jyotika_singh/figure1.png create mode 100644 papers/jyotika_singh/flights.png create mode 100644 papers/jyotika_singh/hotels.png create mode 100644 papers/jyotika_singh/jyotika_singh.rst create mode 100644 papers/jyotika_singh/likes_month.png create mode 100644 papers/jyotika_singh/likes_year.png create mode 100644 papers/jyotika_singh/locs.png create mode 100644 papers/jyotika_singh/stats_shift.png create mode 100644 papers/jyotika_singh/videos.png create mode 100644 papers/jyotika_singh/views_month.png create mode 100644 papers/jyotika_singh/views_year.png diff --git a/papers/jyotika_singh/comments.png b/papers/jyotika_singh/comments.png new 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diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst new file mode 100644 index 0000000000..395e09a95d --- /dev/null +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -0,0 +1,463 @@ +:author: Jyotika Singh +:email: singhjyotika811@gmail.com +:institution: ICX Media, Inc. + +--------------------------------------------------- +Analyzing YouTube using Python and Machine Learning +--------------------------------------------------- + +.. class:: abstract + + Social media is very popularly used every day with daily content viewing + and/or posting that in turn influences people around this world in a variety + of ways. Social media platforms, such as YouTube, have a lot of activity that + goes on every day in terms of video posting, watching and commenting. While + we can open the YouTube app on our phones and look at videos and what people + are commenting, it only gives us a limited view as to kind of things others + around us care about and what is trending amongst other consumers of our + favorite topics or videos. Crawling some of this raw data and performing + analysis on it using NLP can be tricky given the different styles of language + usage by people in today’s world. This talk will highlight the YouTube’s open + Data API and how to use it in python to get the raw data, data cleaning using + Natural Language Processing tricks and Machine Learning in python for social + media interactions, and extraction of trends and key influential factors from + this data in an automated fashion. All these steps towards trend analysis will + be talked about and demonstrated with examples that use different open-source + python tools. + +.. class:: keywords + + nlp, natural language processing, social media data, youtube, named entitity + recognition, ner + + +Introduction +------------ + +Social media has large amounts of activity every second across the globe. Analyzing +text similar to text coming from a social media data source can be tricky due to +the absence of writing style rules and norms. Since this kind of data entails +user written text from a diverse set of locations, writing styles, languages and +topics, it is difficult to normalize data cleaning, extraction and NLP methods. + +Social media data can be extracted using some official and open APIs. Examples +of such APIs include YouTube Data API and Twitter API. One important +thing to note would be to ensure one’s use case fits within compliance of API +guidelines. In this effort, the YouTube Data API will be discussed along +with common gotchas and useful tools that can be leveraged to access data. + +One can perform NLP if the text data type is available for analysis. The nature +of noise seen in text from social media sources will be discussed and presented. +Cleaning of the noisy text using python techniques and open-source packages will +be further analyzed. Social media data additionally entails statistics of content +popularity, likes, dislikes and more. Analysis on statistical and text extracted +from YouTube API will be discussed and evaluated. + +Finally, trend analysis will be performed using open-source python tools, +social media data, statistics, NLP techniques for data cleaning and named entity +recognition (NER) for a story-telling analytics piece. + + +Natural Language Processing +--------------------------- + +Natural language processing (NLP) is the computer manipulation of natural language. +Natural language refers to language coming from a human, either written or spoken. +[enwiki]_ defined NLP as follows: NLP is a subfield of linguistics, computer science, +and artificial intelligence concerned with the interactions between computers and +human language, in particular how to program computers to process and analyze +large amounts of natural language data. The result is a computer capable of +"understanding" the contents of documents, including the contextual nuances of +the language within them. At one extreme, it could be as simple as counting word +frequencies to compare different writing styles. +[bird]_ mentions, "At the other extreme, NLP involves “understanding” complete +human utterances, at least to the extent of being able to give useful responses +to them. NLP is challenging because Natural language is messy. There are few +rules and human language is ever evolving.". + +Some of the common NLP tasks on text data include the following. + +1. Named Entity Recognition + + Named-entity recognition (NER) (also known as (named) entity identification, + entity chunking, and entity extraction) is a subtask of information extraction + that seeks to locate and classify named entities mentioned in unstructured text + into predefined categories such as person names, organizations, locations, + medical codes, time expressions, quantities, monetary values, percentages, etc. + Some popular python libraries that can be leveraged include SpaCy [spacy]_ and NLTK [bird]_. + +2. Keyphrase extraction + + Keyphrase extraction is the task of automatically selecting a small set of + phrases that best describe a given free text document. [CoNLL]_ + Some popular tools that can be used for Keyphrase extraction are mentioned in + this article [#]_. + + .. [#] https://towardsdatascience.com/extracting-keyphrases-from-text-rake-and-gensim-in-python-eefd0fad582f + +3. Unigrams/Bigrams/Trigrams analysis + + Breaking down text into single words, a pair of consecutive written words + or three consecutively written words and analyzing occurrence patterns. + +4. Custom classifier building (public dataset -> features -> ML models) + + If out-of-box solutions do not exist for one's NLP task, building custom + models to help solve for the problem is an option with the help of available + data, NLP libraries (such as NLTK [#]_, SpaCy [#]_ and gensim [#]_), and + Machine Learning libraries (scikit-learn [#]_). + + .. [#] https://www.nltk.org/ + .. [#] https://spacy.io/ + .. [#] https://radimrehurek.com/gensim/ + .. [#] https://scikit-learn.org/ + +5. Others + + Tokenization, Part-of-speech tagging, Lemmatization & Stemming, Word Sense + Disambiguation, Topic modeling, Sentiment Analysis and Text summarization are + some other popularly used NLP tasks. This list is not all inclusive. + + +Potential use cases include the following. + +1. Analytics, intelligence and trends + + Analyzing patterns in text based on word occurrences, language usage, combining + text occurrences with other available data, topics and sentiment information, + NLP method outputs, or combinations thereof. + +2. Story Telling + + Analyzing text using the various NLP techniques along with other statistical + and available data aids in converting raw data to an informative story piece + that helps understand and make sense of patterns observed. Depending on the + data available, a time-window analysis can help study patterns as they change. + +3. Mass analysis + + A human can only see N number of text samples a day to learn, whereas a machine + can analyze a lot greater than N. Leveraging machines for NLP tasks along with + several processing solutions available with Python, such as multiprocessing [#]_, + can help analyze large amounts of data in a reasonable time-frame. + +.. [#] https://docs.python.org/3/library/multiprocessing.html + +Social Media APIs +----------------- + +There are several social media platforms that let you programmatically collect +publicly available data and/or your own published data via APIs. Whatever you +intend to do with this data, it is important to ensure that you use the data in +compliance with the API’s guidelines and terms and services. + +Some types of available requests on YouTube include search, video, channel, +comments, etc. +YouTube Data API documentation [#]_ is a great resource to learn more and get started. +At a high level, the getting started [#]_ steps include registering a project, +enabling the project and using the API key generated. + +.. [#] https://developers.google.com/youtube/v3/docs +.. [#] https://developers.google.com/youtube/v3/getting-started + +Gotchas +~~~~~~~ + +There are a few items to keep in mind when using the YouTube Data API. Some of +the gotchas while using the api include the following. + +1. Rate limits + + The API key registered to you comes with a daily quota. The quota-spend depends + on the kind of requests you make. API does not warn you in API request response + if you are about to finish your daily quota but does throw that error once you + have exceeded the daily quota. It is important to know how your application will + behave if you hit the quota to avoid unexpected behavior and premature script + termination. + +2. Error handling + + If trying to query for a video, comment or channel that is set to private by the + owner, the API throws an error. Your code could end prematurely if you are + querying in a loop and one or a few of the requests have that issue. Error + handling could help automate one’s process better on such expected errors. + +Interacting with the YouTube Data API +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +There are several ways to interact with the YouTube Data API. Some of them are +as follows. + +1. Use the API web explorer's "Try this API" section [#]_ + +2. Build your own code using API documentation examples [#]_ + +3. Open-source tools + + 1. Wrappers of YouTube Data API [#]_ : Libraries that act as wrappers and + provide a way to use YouTube Data API V3. + + 2. pyYouTubeAnalysis [#]_ : This library allows one to run searches, collect + comments and define search params (search keywords, timeframe and type). + Furthermore, the project includes error handling so code execution does not + stop due to unforeseen errors while interacting with YouTube API. Additional + features include NLP methods such as SpaCy based Named Entity Recognition (NER) + that runs location extraction on text. + +.. [#] https://developers.google.com/youtube/v3/docs/search/list +.. [#] https://developers.google.com/youtube/v3/quickstart/python +.. [#] https://github.com/rohitkhatri/youtube-python, https://github.com/sns-sdks/python-youtube +.. [#] https://github.com/jsingh811/pyYouTubeAnalysis + + +Social media / YouTube data noise +--------------------------------- +Text fields are available within several places on YouTube, including video title, +description, tags, comments, channel title and channel description. Video title, +description, tags and channel title and description are filled by the +content/channel owner. Comments on the other hand are made by individuals +reacting to a video. + +.. figure:: comments.png + :scale: 60% + :figclass: w + + Random sample of YouTube comments representing writing style diversity. + :label:`commentsfig` + +The challenges in such a data source arise due to writing style diversity, +language diversity and topic diversity. Figure :ref:`commentsfig` shows a few +examples of language diversity. On social media, people use abbreviations, and +sometimes these abbreviations may not be the most popular ones. Other than the +non-traditional abbreviation usage, different languages, different text lengths +and emojis used by commenters are observed. + +Data Cleaning Techniques +~~~~~~~~~~~~~~~~~~~~~~~~ + +Based on some noise seen on YouTube and other social media platforms, the +following data cleaning techniques have been found to be helpful cleaning methods. + +1. Removing URLs + + Social media text data comes with a lot of URLs. Depending on the task at hand, + removing the urls have been observed to come in handy for cleaning the text. + + .. code-block:: python + + import re + + URL_PATTERN = re.compile( + r"https?://\S+|www\.\S+", + re.X + ) + + def remove_urls(txt): + """ + Remove urls from input text + """ + clean_txt = URL_PATTERN.sub(" ", txt) + return clean_txt + + +2. Removing emojis + + Emojis are widely used across social media and provide benefit in certain NLP + tasks, such as sentiment analysis. On the contrary, for many other NLP tasks, + removing emojis from text can be a useful cleaning method. + + .. code-block:: python + + import re + + EMOJI_PATTERN = re.compile( + "[\U00010000-\U0010ffff]", + flags=re.UNICODE + ) + + + def remove_emojis(txt): + """ + Remove emojis from input text + """ + clean_txt = EMOJI_PATTERN.sub(" ", txt) + return clean_txt + + +3. Spelling / typo corrections + + Some NLP models tend to do very well for a particular style of language and + word usage. On social media, the language seen can be accompanied with + various incorrectly spelled words, also known as typos. + PySpellChecker [#]_, Autocorrect [#]_ and Textblob [#]_ are examples of open-source + tools that can be used for spelling corrections. + +.. [#] https://pypi.org/project/pyspellchecker/ +.. [#] https://pypi.org/project/autocorrect/ +.. [#] https://textblob.readthedocs.io/en/dev/ + + +4. Language detection and translations + + Developing NLP methods on different languages is a challenging and popular + problem. Often when one has developed NLP methods for english language text, + detection of a foreign language and translation to english serves as a good + solution and allows one to keep their NLP methods fixed. Such tasks introduce + other challenges such as the quality of language detection and translation. + Nonetheless, detection and translation is a popular technique while dealing + with multiple different languages. + Some examples of Python libraries that can be used for language detection + include langdetect [#]_, Pycld2 [#]_, Textblob [#]_ and Googletrans [#]_. + Translate [#]_ and Googletrans can be used for language translations. + +.. [#] https://pypi.org/project/langdetect/ +.. [#] https://pypi.org/project/pycld2/ +.. [#] https://textblob.readthedocs.io/en/dev/ +.. [#] https://pypi.org/project/googletrans/ +.. [#] https://pypi.org/project/translate/ + + +Trend analysis case study +--------------------------- + +.. figure:: flights.png + + Domestic and international flight search patterns in 2020. + :label:`flightsfig` + +In the year 2020, COVID hit us all hard. The world went through a lot of changes +in the matter of no time to reduce the spread of the virus. One such impact was +observed massively in the travel and hospitality industry. Figure :ref:`flightsfig` +shows the flight search trends between February and November 2020 for domestic and +international flight searches from the US. Right before lockdown and restrictions +were enforced starting in March across different places across the globe, a big +spike can be seen in flight searches, correlating with the activity of people +trying to fly back home if they were elsewhere before restrictions disabled +them to do so. A massive reduction in flight searches can be seen after March and +beyond as travel was reduced due to COVID imposed restrictions. + +.. figure:: hotels.png + + Hotel booking search patterns in 2020. + :label:`hotelfig` + +Aligning with reduced flight searches, reduced hotel search were also reports +from March onwards as can be seen in figure :ref:`hotelfig` + +Let’s try to correlate these findings and understand content consumption within +those time periods on YouTube. + +First, a search was performed to gather videos about “travel vlogs”. Travel vlogs +are a popular content genre on YouTube where a lot of people are able to find +reviews, advice and sneak peaks of different destinations that wows them and +inspires travel plans. Such videos typically consist of people traveling to +different locations and recording themselves at different spots. + +.. figure:: views_year.png + + Yearly video views. :label:`viewsyearfig` + +.. figure:: likes_year.png + + Yearly video likes. :label:`likesyearfig` + +.. figure:: comments_year.png + + Yearly video comments. :label:`commentsyearfig` + +Statistically, it can be seen from figures :ref:`viewsyearfig`, :ref:`likesyearfig` +and :ref:`commentsyearfig` that travel vlog has been a growing +topic of interest and has been growing along with online content consumption over +the years up till 2019. A downward trend was seen in average views, comments and +likes on travel vlog videos in 2020. + +.. figure:: views_month.png + + Monthly video views for 2019 and 2020. :label:`viewsmonthfig` + +.. figure:: likes_month.png + + Monthly video likes for 2019 and 2020. :label:`likesmonthfig` + +.. figure:: comments_month.png + + Monthly video comments for 2019 and 2020. :label:`commentsmonthfig` + +.. figure:: stats_shift.png + + Difference in video engagements between 2019 and 2020. :label:`statsshiftfig` + + +Figures :ref:`viewsmonthfig`, :ref:`likesmonthfig` and :ref:`commentsmonthfig` +show a month over month comparison between 2019 +and 2020 to analyze average audience engagement patterns. The viewership trends +reflect the reduction from March onwards when COVID hit most locations across the +globe. Figure :ref:`statsshiftfig` further shows engagement shift between 2019 +and 2020. The trend slopes downwards, picks up a little July onwards, which +correlates with the time Europe lifted a lot of the travel restrictions. It can +be seen however, people were still creating travel vlogs and commenting on such +videos. Amidst a much-reduced travel, what were these videos, who was creating +them and what the commenters were talking about? + +.. figure:: videos.png + + Word cloud of video topics. + :label:`videofig` + +Figure :ref:`videofig` shows a word cloud representation of what these videos +were about. Travel that would entail easier implementation of social distance +was seen popping up in 2020, such as hiking, beach trips and road traveling. +Location names such as Italy, France and Spain were also seen showing up in the videos. + +.. figure:: locs.png + + Word cloud of location names used in comments. + :label:`locsfig` + +A few examples of comments that were being left by audiences of such videos are +as follows. + + "i’m going to sorrento in 10 days and i’m so excited. i’ve been watching tonnes + of sorrento and italy vlogs and yours are so lush X) <3" + + "Did they require you to have a prior covid test?" + + "I loved the tour looked like you guys had fun. im going there next week, how + long ago were you there and were there lots of restrictions and closing due to + covid" + + "Great video man, this place looks amazing. I have never been to Iceland, would + love to visit some day. Honestly can't wait for the lockdown to be lifted so I + can start travelling again. Thanks for sharing your experience. :)" + +It was seen that people expressed interest in inquiring about the lifting of the +travel ban due to COVID, pre-travel COVID test requirements along with the +sentiment of waiting to travel again. People were seen mentioning a lot of +location names in their comments. With the help of SpaCy’s NER, location +extractions were performed and figure :ref:`locsfig` shows the location popularly +mentioned by commenters. One can see European locations, along with some Asian +and American locations which correlate with travel restriction reductions in +some of the places. + +This analysis, including data collection and NER, was performed +using pyYouTubeAnalysis library [#]_. + +.. [#] https://github.com/jsingh811/pyYouTubeAnalysis + + +References +---------- +.. [enwiki] Wikipedia contributors. "Natural language processing." Wikipedia, + The Free Encyclopedia. Wikipedia, The Free Encyclopedia, + 21 May. 2021. Web. 27 May. 2021. + +.. [bird] Bird, Steven, et al. Natural Language Processing with Python. + O’Reilly Media, 2009. + +.. [CoNLL] Bennani-Smires, Kamil & Musat, Claudiu & Hossmann, Andreea & Baeriswyl, + Michael & Jaggi, Martin. (2018). Simple Unsupervised Keyphrase Extraction + using Sentence Embeddings. 10.18653/v1/K18-1022. + +.. [spacy] Honnibal, Matthew and Montani, Ines and Van Landeghem, Sofie and Boyd, Adriane. + (2020). spaCy: Industrial-strength Natural Language Processing in Python. + Zenodo. 10.5281/zenodo.1212303. https://doi.org/10.5281/zenodo.1212303. diff --git a/papers/jyotika_singh/likes_month.png b/papers/jyotika_singh/likes_month.png new file mode 100644 index 0000000000000000000000000000000000000000..3b223a275717918004e07ecc1699afb760f4fa56 GIT binary patch literal 26651 zcmd?RWmHw$8#lV?l1`QGMiEe?5u`)uZk67ENaF@+1*BC3q@=sHpmc*EE!`p=l2Ugr z{htHp9q*U>?T&HB;COHed(XYrn$PpAXN9V(%HiW)!-c_M_zLne4`DFWLKqB16B`p8 zS){F90RKaAc_=3hE9{|O27keU%j>zoU=%FSe<%tMnYUmtNtl9+l%_k%S`wBw+0a<) z^zMBUPrlTHh=*h?{vr4>>KWhbBT$_mAkz>ZbfXwmtWhhwv}7>0eZxZhHJ87Pd}GK+ zm^}RrjEuaqv$Jh@Q?1gTX->_KC~KX1vhZ4SkB#beTx#QU3iB z0SDJ5XIcL}0KS=KlAK-f@@c#lb?H2OXONt=!NR?L`Pd;X3a-xdD(P^@rDfI=fP*6H z$k5Bj4zSgczl~Fh?_Zn=Nz`ZH;Fw>d3Uq7+8|7uooV-9Q!=JUmNx_TU_OlcM|K=w< zvo>0`Gi>145zV56nm23516>~XDDW96XUm>IE2e*)l*D7v77?c=e$?y`*gr5}RgXaF 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ziVF}{MDBljnQ<{HbTx7X@e^QPs-Ea6GGUpW#tVH!9<`+(-1vDZKiW^#89$&t@Bzfa zz9OAu_~FXLd=$Uw-p*xgsbkPM-m4R6T8iR$LhD;Ph6Mgw+|yTSLV$o)ev=>yw2B_; z1Bewt!NmS*MzFBe0<`7aCk?0Q#5ylwP&V5`&}ev(c5_!`8eFM}jaYFO+jQPqs~b3- z7km6?2UBscu)rkSg05cZo$Y6BHagYb6hc2&%DCN7RNUl89!^op=wtM!7tEc5m}gXA z^SUCKFeX>DIAit?SYK2zomv&jPrgGtP%K1^VOXO99Hk7RB?=ThgLwW6G&ReA9w#Oc zp;V89WXS9xGQ9Mwl5DLFA~fjoPP)6|d0(d3IDNo~h!tvWeHlR2+;`#$qxDqT|K^l% z><%AK{|^6a)=E7Sh$pza;{NwTbT`KUoV3ojU9Dx?G+0Dfx9k4+JT5~ri6?!VjP@Fa fCw=zX>iY~zp2pUw;cc}5mi;apw4HSg>T1NlO7Bmv literal 0 HcmV?d00001 From 630f032ef18a4659c905386b50ab2aef5f981c4d Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Fri, 28 May 2021 16:54:21 -0700 Subject: [PATCH 02/15] Add figure references --- papers/jyotika_singh/jyotika_singh.rst | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 395e09a95d..2e409bedec 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -333,17 +333,21 @@ international flight searches from the US. Right before lockdown and restriction were enforced starting in March across different places across the globe, a big spike can be seen in flight searches, correlating with the activity of people trying to fly back home if they were elsewhere before restrictions disabled -them to do so. A massive reduction in flight searches can be seen after March and +them to do so. A massive reduction in flight searches [#]_ can be seen after March and beyond as travel was reduced due to COVID imposed restrictions. +.. [#] https://www.kayak.com/news/category/travel-trends/ + .. figure:: hotels.png Hotel booking search patterns in 2020. :label:`hotelfig` -Aligning with reduced flight searches, reduced hotel search were also reports +Aligning with reduced flight searches, reduced hotel search [#]_ were also reports from March onwards as can be seen in figure :ref:`hotelfig` +.. [#] https://www.sojern.com/blog/covid-19-insights-on-travel-impact-hotel-agency/ + Let’s try to correlate these findings and understand content consumption within those time periods on YouTube. @@ -396,8 +400,8 @@ globe. Figure :ref:`statsshiftfig` further shows engagement shift between 2019 and 2020. The trend slopes downwards, picks up a little July onwards, which correlates with the time Europe lifted a lot of the travel restrictions. It can be seen however, people were still creating travel vlogs and commenting on such -videos. Amidst a much-reduced travel, what were these videos, who was creating -them and what the commenters were talking about? +videos. Amidst a much-reduced travel, what were these videos, what content was +getting created and what the commenters were talking about? .. figure:: videos.png From c1898bf426f53b159fa589eefc143453eba49a87 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Fri, 28 May 2021 17:15:13 -0700 Subject: [PATCH 03/15] Add influencer urls --- papers/jyotika_singh/jyotika_singh.rst | 26 ++++++++++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 2e409bedec..c209e5c17a 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -400,8 +400,9 @@ globe. Figure :ref:`statsshiftfig` further shows engagement shift between 2019 and 2020. The trend slopes downwards, picks up a little July onwards, which correlates with the time Europe lifted a lot of the travel restrictions. It can be seen however, people were still creating travel vlogs and commenting on such -videos. Amidst a much-reduced travel, what were these videos, what content was -getting created and what the commenters were talking about? +videos. Between June and September 2020, amidst a much-reduced travel, what were +these videos, what content was getting created, who was creating it and what were +the commenters talking about? .. figure:: videos.png @@ -413,6 +414,27 @@ were about. Travel that would entail easier implementation of social distance was seen popping up in 2020, such as hiking, beach trips and road traveling. Location names such as Italy, France and Spain were also seen showing up in the videos. +YouTube influencer channels that drove high engagement during summer and fall of 2020 +include the following. + +1. 4K Walk [#]_ – YouTube channel creating videos about walking tours all over Europe +and America. + +2. BeachTuber [#]_ – YouTube channel creating vlogs from different beaches all over Europe. + +3. Beach Walk [#]_ – YouTube channel posting about different beaches all over Europe and +America. + +4. DesiGirl Traveller [#]_ – YouTube channel creating videos about India travel. + +5. Euro Trotter [#]_ – YouTube channel creating videos about Europe travel. + +.. [#] https://youtube.com/c/4KWALK +.. [#] https://youtube.com/c/BeachTuber +.. [#] https://youtube.com/c/BeachWalk +.. [#] https://youtube.com/c/DesiGirlTraveller +.. [#] https://youtube.com/c/EuroTrotter + .. figure:: locs.png Word cloud of location names used in comments. From 8bd1d8b470a27ff6901dfcf243833025c318b592 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Fri, 28 May 2021 21:28:07 -0700 Subject: [PATCH 04/15] Fix index --- papers/jyotika_singh/jyotika_singh.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index c209e5c17a..7dc6cca392 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -417,13 +417,11 @@ Location names such as Italy, France and Spain were also seen showing up in the YouTube influencer channels that drove high engagement during summer and fall of 2020 include the following. -1. 4K Walk [#]_ – YouTube channel creating videos about walking tours all over Europe -and America. +1. 4K Walk [#]_ – YouTube channel creating videos about walking tours all over Europe and America. 2. BeachTuber [#]_ – YouTube channel creating vlogs from different beaches all over Europe. -3. Beach Walk [#]_ – YouTube channel posting about different beaches all over Europe and -America. +3. Beach Walk [#]_ – YouTube channel posting about different beaches all over Europe and America. 4. DesiGirl Traveller [#]_ – YouTube channel creating videos about India travel. From ed0fc0faa7ec1b9ec36b112ef3b60f81d148edc7 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 8 Jun 2021 16:49:14 -0700 Subject: [PATCH 05/15] Add trend correlation description --- papers/jyotika_singh/jyotika_singh.rst | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 7dc6cca392..790742c528 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -397,8 +397,11 @@ show a month over month comparison between 2019 and 2020 to analyze average audience engagement patterns. The viewership trends reflect the reduction from March onwards when COVID hit most locations across the globe. Figure :ref:`statsshiftfig` further shows engagement shift between 2019 -and 2020. The trend slopes downwards, picks up a little July onwards, which -correlates with the time Europe lifted a lot of the travel restrictions. It can +and 2020. The trend slopes upwards until March hits, which is when a lot of locations +imposed stay at home orders and lockdowns. The trend slopes downwards, picks up a +little July onwards, which correlates with the time Europe lifted a lot of the travel +restrictions. The chart representing "travel vlog" content engagement largely +correlates with the flight search trend as shown in figure :ref:`flightsfig`. It can be seen however, people were still creating travel vlogs and commenting on such videos. Between June and September 2020, amidst a much-reduced travel, what were these videos, what content was getting created, who was creating it and what were From 860620dbfc618cefb549e6725a26cb2679fc388e Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 8 Jun 2021 17:14:10 -0700 Subject: [PATCH 06/15] Add images --- papers/jyotika_singh/global-flight-searches.jpg | Bin 0 -> 92574 bytes papers/jyotika_singh/global-hotel-searches.jpg | Bin 0 -> 75293 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 papers/jyotika_singh/global-flight-searches.jpg create mode 100644 papers/jyotika_singh/global-hotel-searches.jpg diff --git a/papers/jyotika_singh/global-flight-searches.jpg b/papers/jyotika_singh/global-flight-searches.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ffef577cf04ebada1002d410d0f5547dbe22114f GIT binary patch literal 92574 zcmeEu2UJtrw&(@|0qI?Oq>4!Ip@xoBX;N$<1PGyq4k8K)3Id9VN)aiFihy)!QUwvE 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zwnI5@WmNmn`NeoREQUZ3$Ag?CgywNlgH|upVB$W(0uzg3_vqKtxR&~9iL(RB;80Cv!3*P; z-f4!>5y^*BGiNyB6R?VqRgMnY^h#LKKL+b7t+pYP-jFZ&vC2(f5Dq+k-lkn3vO%%2 zt>!F+qCTTSbgGan)mR?M8Vj?DX*ngaE247!VW0t-{oHAD{qE5IuV-ArN3+=#O!vZX z@Ol?jO|OfMx80hUnOS08N_;7!6*qw7NHMt85$kCGM{vzy_&FYEwT1P$D`b?;>v#G_ jZ12-#)T}J5EmZo*Pb;?q;42n>{{_7DKROlp*W|weLTrgv literal 0 HcmV?d00001 From 12e0d91f1ee231e2c239308f3082924cd5ac00b8 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 8 Jun 2021 17:26:50 -0700 Subject: [PATCH 07/15] Add ref to new figures --- papers/jyotika_singh/jyotika_singh.rst | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 790742c528..be0183d745 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -325,26 +325,32 @@ Trend analysis case study Domestic and international flight search patterns in 2020. :label:`flightsfig` +.. figure:: global-flight-searches.jpg + + Global flight search patterns in 2020. + :label:`flightsfigglobal` + In the year 2020, COVID hit us all hard. The world went through a lot of changes in the matter of no time to reduce the spread of the virus. One such impact was observed massively in the travel and hospitality industry. Figure :ref:`flightsfig` -shows the flight search trends between February and November 2020 for domestic and +[#]_ shows the flight search trends between February and November 2020 for domestic and international flight searches from the US. Right before lockdown and restrictions were enforced starting in March across different places across the globe, a big spike can be seen in flight searches, correlating with the activity of people trying to fly back home if they were elsewhere before restrictions disabled -them to do so. A massive reduction in flight searches [#]_ can be seen after March and -beyond as travel was reduced due to COVID imposed restrictions. +them to do so. A massive reduction in flight searches can be seen in figure +:ref:`flightsfig`and figure :ref:`flightsfigglobal` after March and +beyond as travel was reduced due to COVID imposed events and restrictions. .. [#] https://www.kayak.com/news/category/travel-trends/ -.. figure:: hotels.png +.. figure:: global-hotel-searches.jpg Hotel booking search patterns in 2020. :label:`hotelfig` -Aligning with reduced flight searches, reduced hotel search [#]_ were also reports -from March onwards as can be seen in figure :ref:`hotelfig` +Aligning with reduced flight searches, reduced hotel search were also reported +from March onwards as can be seen in figure :ref:`hotelfig` [#]_. .. [#] https://www.sojern.com/blog/covid-19-insights-on-travel-impact-hotel-agency/ From 961d18460c98f30a26e82cd4fbe29f81cb4e7c4a Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 8 Jun 2021 17:59:33 -0700 Subject: [PATCH 08/15] Add citations --- papers/jyotika_singh/jyotika_singh.rst | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index be0183d745..352a85ab12 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -289,12 +289,11 @@ following data cleaning techniques have been found to be helpful cleaning method Some NLP models tend to do very well for a particular style of language and word usage. On social media, the language seen can be accompanied with various incorrectly spelled words, also known as typos. - PySpellChecker [#]_, Autocorrect [#]_ and Textblob [#]_ are examples of open-source + PySpellChecker [OpenSubtitles2016]_ [#]_, Autocorrect [#]_ and Textblob [textblob]_ are examples of open-source tools that can be used for spelling corrections. .. [#] https://pypi.org/project/pyspellchecker/ .. [#] https://pypi.org/project/autocorrect/ -.. [#] https://textblob.readthedocs.io/en/dev/ 4. Language detection and translations @@ -307,12 +306,10 @@ following data cleaning techniques have been found to be helpful cleaning method Nonetheless, detection and translation is a popular technique while dealing with multiple different languages. Some examples of Python libraries that can be used for language detection - include langdetect [#]_, Pycld2 [#]_, Textblob [#]_ and Googletrans [#]_. + include langdetect [langdetect]_, Pycld2 [#]_, Textblob [textblob]_ and Googletrans [#]_. Translate [#]_ and Googletrans can be used for language translations. -.. [#] https://pypi.org/project/langdetect/ .. [#] https://pypi.org/project/pycld2/ -.. [#] https://textblob.readthedocs.io/en/dev/ .. [#] https://pypi.org/project/googletrans/ .. [#] https://pypi.org/project/translate/ @@ -494,3 +491,18 @@ References .. [spacy] Honnibal, Matthew and Montani, Ines and Van Landeghem, Sofie and Boyd, Adriane. (2020). spaCy: Industrial-strength Natural Language Processing in Python. Zenodo. 10.5281/zenodo.1212303. https://doi.org/10.5281/zenodo.1212303. + +.. [textblob] Loria, Steven. (2018). textblob Documentation. Release 0.15, 2. + https://textblob.readthedocs.io/en/dev/. + +.. [langdetect] Shuyo, Nakatani. (2010). Language Detection Library for Java. + http://code.google.com/p/language-detection/, + https://pypi.org/project/langdetect/. + +.. [OpenSubtitles2016] P Lison, J Tiedemann. (2016). + OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. + In Proceedings of the 10th International Conference on Language Resources and Evaluation + (LREC 2016). https://www.aclweb.org/anthology/L16-1147. + + + From c9e6e82516d9237511a03e1a4b624cc81bf93e27 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 8 Jun 2021 22:16:50 -0700 Subject: [PATCH 09/15] Update citations --- papers/jyotika_singh/jyotika_singh.rst | 29 +++++++++++++++----------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 352a85ab12..bb8b88c3f3 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -197,8 +197,8 @@ as follows. 1. Wrappers of YouTube Data API [#]_ : Libraries that act as wrappers and provide a way to use YouTube Data API V3. - 2. pyYouTubeAnalysis [#]_ : This library allows one to run searches, collect - comments and define search params (search keywords, timeframe and type). + 2. pyYouTubeAnalysis [pyYouTubeAnalysis]_ [#]_ : This library allows one to + run searches, collect comments and define search params (search keywords, timeframe and type). Furthermore, the project includes error handling so code execution does not stop due to unforeseen errors while interacting with YouTube API. Additional features include NLP methods such as SpaCy based Named Entity Recognition (NER) @@ -354,11 +354,12 @@ from March onwards as can be seen in figure :ref:`hotelfig` [#]_. Let’s try to correlate these findings and understand content consumption within those time periods on YouTube. -First, a search was performed to gather videos about “travel vlogs”. Travel vlogs -are a popular content genre on YouTube where a lot of people are able to find -reviews, advice and sneak peaks of different destinations that wows them and -inspires travel plans. Such videos typically consist of people traveling to -different locations and recording themselves at different spots. +First, a search was performed to gather videos about “travel vlogs” using the +pyYouTubeAnalysis [pyYouTubeAnalysis]_. Travel vlogs are a popular content +genre on YouTube where a lot of people are able to find reviews, advice +and sneak peaks of different destinations that wows them and inspires travel +plans. Such videos typically consist of people traveling to different locations +and recording themselves at different spots. .. figure:: views_year.png @@ -470,7 +471,7 @@ and American locations which correlate with travel restriction reductions in some of the places. This analysis, including data collection and NER, was performed -using pyYouTubeAnalysis library [#]_. +using pyYouTubeAnalysis library [pyYouTubeAnalysis]_ [#]_. .. [#] https://github.com/jsingh811/pyYouTubeAnalysis @@ -486,18 +487,22 @@ References .. [CoNLL] Bennani-Smires, Kamil & Musat, Claudiu & Hossmann, Andreea & Baeriswyl, Michael & Jaggi, Martin. (2018). Simple Unsupervised Keyphrase Extraction - using Sentence Embeddings. 10.18653/v1/K18-1022. + using Sentence Embeddings. 10.18653/v1/K18-1022 .. [spacy] Honnibal, Matthew and Montani, Ines and Van Landeghem, Sofie and Boyd, Adriane. (2020). spaCy: Industrial-strength Natural Language Processing in Python. - Zenodo. 10.5281/zenodo.1212303. https://doi.org/10.5281/zenodo.1212303. + Zenodo. 10.5281/zenodo.1212303. https://doi.org/10.5281/zenodo.1212303 + +.. [pyYouTubeAnalysis] Singh, J. (2021). jsingh811/pyYouTubeAnalysis: + pyYouTubeAnalysis: YouTube data requests and NER on text (v1.0) [Computer software]. Zenodo. + 10.5281/ZENODO.4915746. https://doi.org/10.5281/ZENODO.4915746 .. [textblob] Loria, Steven. (2018). textblob Documentation. Release 0.15, 2. - https://textblob.readthedocs.io/en/dev/. + https://textblob.readthedocs.io/en/dev/ .. [langdetect] Shuyo, Nakatani. (2010). Language Detection Library for Java. http://code.google.com/p/language-detection/, - https://pypi.org/project/langdetect/. + https://pypi.org/project/langdetect/ .. [OpenSubtitles2016] P Lison, J Tiedemann. (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. From 1b1f77613ea80e9860a248b39404615edb20240b Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Wed, 9 Jun 2021 15:36:26 -0700 Subject: [PATCH 10/15] Improve image resolutions --- papers/jyotika_singh/comments.png | Bin 101002 -> 165604 bytes papers/jyotika_singh/figure1.png | Bin 18932 -> 0 bytes papers/jyotika_singh/flights_s.png | Bin 0 -> 239149 bytes .../jyotika_singh/global-flight-searches.jpg | Bin 92574 -> 0 bytes .../jyotika_singh/global-hotel-searches.jpg | Bin 75293 -> 0 bytes papers/jyotika_singh/hotels.png | Bin 40286 -> 219988 bytes papers/jyotika_singh/jyotika_singh.rst | 99 +++++++++++------- papers/jyotika_singh/yearly_stats.png | Bin 0 -> 40140 bytes 8 files changed, 63 insertions(+), 36 deletions(-) delete mode 100644 papers/jyotika_singh/figure1.png create mode 100644 papers/jyotika_singh/flights_s.png delete mode 100644 papers/jyotika_singh/global-flight-searches.jpg delete mode 100644 papers/jyotika_singh/global-hotel-searches.jpg create mode 100644 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z0DYNt6`8!+5W7H4F*plHTI_T9dz1a@&Eu9O0qXb&h?mM?9jd5GahZf1EA)z99{fHxakZD?Wu3QHSPT#p*cX?}M$M~n86dTqR z$vVwRNXWjzK*tTGe4ivZ=^+34jvJ>Ul5)P6LTsEm>8bPsfc@XA%cHu|Sf4zl4a=s0inS>r2qf` diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index bb8b88c3f3..b5e020b82c 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -77,14 +77,15 @@ rules and human language is ever evolving.". Some of the common NLP tasks on text data include the following. -1. Named Entity Recognition +1. Named entity recognition Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. - Some popular python libraries that can be leveraged include SpaCy [spacy]_ and NLTK [bird]_. + Some popular python libraries that can be leveraged include SpaCy [spacy]_ and + NLTK [bird]_. 2. Keyphrase extraction @@ -127,7 +128,7 @@ Potential use cases include the following. text occurrences with other available data, topics and sentiment information, NLP method outputs, or combinations thereof. -2. Story Telling +2. Story telling Analyzing text using the various NLP techniques along with other statistical and available data aids in converting raw data to an informative story piece @@ -198,11 +199,11 @@ as follows. provide a way to use YouTube Data API V3. 2. pyYouTubeAnalysis [pyYouTubeAnalysis]_ [#]_ : This library allows one to - run searches, collect comments and define search params (search keywords, timeframe and type). - Furthermore, the project includes error handling so code execution does not - stop due to unforeseen errors while interacting with YouTube API. Additional - features include NLP methods such as SpaCy based Named Entity Recognition (NER) - that runs location extraction on text. + run searches, collect comments and define search params (search keywords, + timeframe and type). Furthermore, the project includes error handling so code + execution does not stop due to unforeseen errors while interacting with + YouTube API. Additional features include NLP methods such as SpaCy based + Named Entity Recognition (NER) that runs location extraction on text. .. [#] https://developers.google.com/youtube/v3/docs/search/list .. [#] https://developers.google.com/youtube/v3/quickstart/python @@ -210,21 +211,22 @@ as follows. .. [#] https://github.com/jsingh811/pyYouTubeAnalysis -Social media / YouTube data noise +Social Media / YouTube Data Noise --------------------------------- -Text fields are available within several places on YouTube, including video title, -description, tags, comments, channel title and channel description. Video title, -description, tags and channel title and description are filled by the -content/channel owner. Comments on the other hand are made by individuals -reacting to a video. .. figure:: comments.png - :scale: 60% + :scale: 42% :figclass: w Random sample of YouTube comments representing writing style diversity. :label:`commentsfig` +Text fields are available within several places on YouTube, including video title, +description, tags, comments, channel title and channel description. Video title, +description, tags and channel title and description are filled by the +content/channel owner. Comments on the other hand are made by individuals +reacting to a video. + The challenges in such a data source arise due to writing style diversity, language diversity and topic diversity. Figure :ref:`commentsfig` shows a few examples of language diversity. On social media, people use abbreviations, and @@ -289,8 +291,9 @@ following data cleaning techniques have been found to be helpful cleaning method Some NLP models tend to do very well for a particular style of language and word usage. On social media, the language seen can be accompanied with various incorrectly spelled words, also known as typos. - PySpellChecker [OpenSubtitles2016]_ [#]_, Autocorrect [#]_ and Textblob [textblob]_ are examples of open-source - tools that can be used for spelling corrections. + PySpellChecker [OpenSubtitles2016]_ [#]_, Autocorrect [#]_ and Textblob + [textblob]_ are examples of open-source tools that can be used for spelling + corrections. .. [#] https://pypi.org/project/pyspellchecker/ .. [#] https://pypi.org/project/autocorrect/ @@ -314,34 +317,41 @@ following data cleaning techniques have been found to be helpful cleaning method .. [#] https://pypi.org/project/translate/ -Trend analysis case study +Trend Analysis Case Study --------------------------- +.. figure:: flights_s.png + :scale: 50% + :figclass: w + + Global flight search patterns in 2020. + :label:`flightsfigglobal` + .. figure:: flights.png + :scale: 100% + :figclass: w Domestic and international flight search patterns in 2020. :label:`flightsfig` -.. figure:: global-flight-searches.jpg - - Global flight search patterns in 2020. - :label:`flightsfigglobal` - In the year 2020, COVID hit us all hard. The world went through a lot of changes in the matter of no time to reduce the spread of the virus. One such impact was observed massively in the travel and hospitality industry. Figure :ref:`flightsfig` [#]_ shows the flight search trends between February and November 2020 for domestic and -international flight searches from the US. Right before lockdown and restrictions +international flight searches from the US using Kayak. Right before lockdown and restrictions were enforced starting in March across different places across the globe, a big spike can be seen in flight searches, correlating with the activity of people trying to fly back home if they were elsewhere before restrictions disabled -them to do so. A massive reduction in flight searches can be seen in figure -:ref:`flightsfig`and figure :ref:`flightsfigglobal` after March and -beyond as travel was reduced due to COVID imposed events and restrictions. +them to do so. A massive reduction in flight searches can be seen in figures +:ref:`flightsfig` and :ref:`flightsfigglobal` [#]_ after March and as travel +was reduced due to COVID imposed events and restrictions. .. [#] https://www.kayak.com/news/category/travel-trends/ +.. [#] https://www.sojern.com/blog/covid-19-insights-on-travel-impact-hotel-agency/ -.. figure:: global-hotel-searches.jpg +.. figure:: hotels.png + :scale: 50% + :figclass: w Hotel booking search patterns in 2020. :label:`hotelfig` @@ -362,14 +372,17 @@ plans. Such videos typically consist of people traveling to different locations and recording themselves at different spots. .. figure:: views_year.png + :scale: 60% Yearly video views. :label:`viewsyearfig` .. figure:: likes_year.png + :scale: 60% Yearly video likes. :label:`likesyearfig` .. figure:: comments_year.png + :scale: 60% Yearly video comments. :label:`commentsyearfig` @@ -428,7 +441,7 @@ include the following. 2. BeachTuber [#]_ – YouTube channel creating vlogs from different beaches all over Europe. -3. Beach Walk [#]_ – YouTube channel posting about different beaches all over Europe and America. +3. Beach Walk [#]_ – YouTube channel posting about different beaches all over Europe and America. 4. DesiGirl Traveller [#]_ – YouTube channel creating videos about India travel. @@ -476,6 +489,22 @@ using pyYouTubeAnalysis library [pyYouTubeAnalysis]_ [#]_. .. [#] https://github.com/jsingh811/pyYouTubeAnalysis +Conclusion +---------- + +Language usage on social media contains mixed writing styles, languages, freeform +emoji and abbreviation usage. This variety makes it harder to perform NLP tasks +on data coming from social media. Described cleaning techniques such as emoji removal, +hyperlink removal, language translations and typo corrections have been found +useful in dealing with language of such nature. + +Social media data contain statistics in addition to text data that measure human +engagement and interest in content. Combining these statistics with inferences +from NLP techniques such as named-entity recognition (NER) are found to be helpful +in trend analysis and analytics around correlations and interests of people +engaging with social media. + + References ---------- .. [enwiki] Wikipedia contributors. "Natural language processing." Wikipedia, @@ -494,20 +523,18 @@ References Zenodo. 10.5281/zenodo.1212303. https://doi.org/10.5281/zenodo.1212303 .. [pyYouTubeAnalysis] Singh, J. (2021). jsingh811/pyYouTubeAnalysis: - pyYouTubeAnalysis: YouTube data requests and NER on text (v1.0) [Computer software]. Zenodo. - 10.5281/ZENODO.4915746. https://doi.org/10.5281/ZENODO.4915746 + pyYouTubeAnalysis: YouTube data requests and NER on text (v1.0) + [Computer software]. Zenodo. 10.5281/ZENODO.4915746. + https://doi.org/10.5281/ZENODO.4915746 .. [textblob] Loria, Steven. (2018). textblob Documentation. Release 0.15, 2. https://textblob.readthedocs.io/en/dev/ .. [langdetect] Shuyo, Nakatani. (2010). Language Detection Library for Java. - http://code.google.com/p/language-detection/, + http://code.google.com/p/language-detection/, https://pypi.org/project/langdetect/ -.. [OpenSubtitles2016] P Lison, J Tiedemann. (2016). +.. [OpenSubtitles2016] P Lison, J Tiedemann. (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. 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z7lZ|*1!m|My!50V3Es0f$hF+0yi6IU5I|xcZGD`{+1_Q4J$#U&Mm5iJ9OhlnfiZlQZ`?Mlo62S(}(60_ZO_(Q;M2oQ@7NWWW4!$9p^d zRe7;$O=$5zNz<1oKI3p^o(Rev0b2_&!J6FWnl_@`LK)WPv8y;(_O`yg^PP53zB9B| zIaG=j)SJ>U@s4Rw;ykhv17@+K6-@^9KE?&)lia3oXxt$bW0B%w0F0tM*R<|&CHNo| z-h+}Ix3qSlsUjp(@OmUue2IhmA8|nK<$lYodD(%bNvr%2qc>0luPx-=f{n1pS3;BR~!frehQN0;krju{4rqnIU|hciFG})SYir zC(W?FtrwY5#*#-BIZWZn*0&SjefJI&=SXO;_W35F*n$JELoTHQd+!=po&0x8!D z4>oWg+kDm5YNOyWzQ-8jyT^)ECK;pzZ3Me{+c;F;VcDA*LUxb~{|8epF>-YdwL|~_ N002ovPDHLkV1nS$;r{>t diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index b5e020b82c..9e91dd64ec 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -16,19 +16,19 @@ Analyzing YouTube using Python and Machine Learning are commenting, it only gives us a limited view as to kind of things others around us care about and what is trending amongst other consumers of our favorite topics or videos. Crawling some of this raw data and performing - analysis on it using NLP can be tricky given the different styles of language - usage by people in today’s world. This talk will highlight the YouTube’s open - Data API and how to use it in python to get the raw data, data cleaning using - Natural Language Processing tricks and Machine Learning in python for social + analysis on it using Natural Language Processing (NLP) can be tricky given + the different styles of language usage by people in today’s world. This effort + highlights the YouTube’s open Data API and how to use it in python to get the + raw data, data cleaning using NLP tricks and Machine Learning in python for social media interactions, and extraction of trends and key influential factors from - this data in an automated fashion. All these steps towards trend analysis will - be talked about and demonstrated with examples that use different open-source + this data in an automated fashion. All these steps towards trend analysis are + discussed and demonstrated with examples that use different open-source python tools. .. class:: keywords nlp, natural language processing, social media data, youtube, named entitity - recognition, ner + recognition, ner, keyphrase extraction Introduction @@ -38,7 +38,8 @@ Social media has large amounts of activity every second across the globe. Analyz text similar to text coming from a social media data source can be tricky due to the absence of writing style rules and norms. Since this kind of data entails user written text from a diverse set of locations, writing styles, languages and -topics, it is difficult to normalize data cleaning, extraction and NLP methods. +topics, it is difficult to normalize data cleaning, extraction, and Natural +Language Processing (NLP) methods. Social media data can be extracted using some official and open APIs. Examples of such APIs include YouTube Data API and Twitter API. One important @@ -84,17 +85,22 @@ Some of the common NLP tasks on text data include the following. that seeks to locate and classify named entities mentioned in unstructured text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. - Some popular python libraries that can be leveraged include SpaCy [spacy]_ and + Some popular Python libraries that can be leveraged to perform named entity + recognition for a variety of different entities include SpaCy [spacy]_ and NLTK [bird]_. 2. Keyphrase extraction Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. [CoNLL]_ - Some popular tools that can be used for Keyphrase extraction are mentioned in - this article [#]_. + Some popular tools that can be used for keyphrase extraction as mentioned in + this article [#]_ include Gensim [rehurek2011gensim]_ and RAKE-NLTK[#]_. + Another way keyphrase extraction can be performed is using NLTK [bird]_ methods. + This implementation is included in the pyYouTubeAnalysis [pyYouTubeAnalysis]_ library. .. [#] https://towardsdatascience.com/extracting-keyphrases-from-text-rake-and-gensim-in-python-eefd0fad582f + .. [#] https://pypi.org/project/rake-nltk/ + .. [#] https://scikit-learn.org/ 3. Unigrams/Bigrams/Trigrams analysis @@ -105,7 +111,7 @@ Some of the common NLP tasks on text data include the following. If out-of-box solutions do not exist for one's NLP task, building custom models to help solve for the problem is an option with the help of available - data, NLP libraries (such as NLTK [#]_, SpaCy [#]_ and gensim [#]_), and + data, NLP libraries (such as NLTK [#]_, SpaCy [#]_, and gensim [#]_), and Machine Learning libraries (scikit-learn [#]_). .. [#] https://www.nltk.org/ @@ -119,30 +125,30 @@ Some of the common NLP tasks on text data include the following. Disambiguation, Topic modeling, Sentiment Analysis and Text summarization are some other popularly used NLP tasks. This list is not all inclusive. +A human can only see N number of text samples a day to learn, whereas a machine +can analyze a lot greater than N. Leveraging machines for NLP tasks along with +several processing solutions available with Python, such as multiprocessing [#]_, +can help analyze large amounts of data in a reasonable time-frame. + +.. [#] https://docs.python.org/3/library/multiprocessing.html Potential use cases include the following. 1. Analytics, intelligence and trends - Analyzing patterns in text based on word occurrences, language usage, combining - text occurrences with other available data, topics and sentiment information, + Analyzing patterns in text based on word occurrences, language, combining + text occurrences with other available data, topics, sentiment information, NLP method outputs, or combinations thereof. 2. Story telling - Analyzing text using the various NLP techniques along with other statistical - and available data aids in converting raw data to an informative story piece - that helps understand and make sense of patterns observed. Depending on the - data available, a time-window analysis can help study patterns as they change. + Analyzing text using the various NLP techniques along with other statistical and + other available data aids in converting raw data to an informative story piece + that helps uncover and understand the patterns that exist within the data. + Depending on the data available, a time-window analysis can help study patterns + as they change with respect to time in terms of word usages, topics, text lengths, + or combinations thereof. -3. Mass analysis - - A human can only see N number of text samples a day to learn, whereas a machine - can analyze a lot greater than N. Leveraging machines for NLP tasks along with - several processing solutions available with Python, such as multiprocessing [#]_, - can help analyze large amounts of data in a reasonable time-frame. - -.. [#] https://docs.python.org/3/library/multiprocessing.html Social Media APIs ----------------- @@ -152,11 +158,13 @@ publicly available data and/or your own published data via APIs. Whatever you intend to do with this data, it is important to ensure that you use the data in compliance with the API’s guidelines and terms and services. -Some types of available requests on YouTube include search, video, channel, -comments, etc. +Some types of available requests on YouTube include search, video, channel and +comments. + YouTube Data API documentation [#]_ is a great resource to learn more and get started. At a high level, the getting started [#]_ steps include registering a project, -enabling the project and using the API key generated. +enabling the project and using the API key generated. With this key, the user can +start making requests to the API to crawl data. .. [#] https://developers.google.com/youtube/v3/docs .. [#] https://developers.google.com/youtube/v3/getting-started @@ -183,6 +191,7 @@ the gotchas while using the api include the following. querying in a loop and one or a few of the requests have that issue. Error handling could help automate one’s process better on such expected errors. + Interacting with the YouTube Data API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -198,12 +207,14 @@ as follows. 1. Wrappers of YouTube Data API [#]_ : Libraries that act as wrappers and provide a way to use YouTube Data API V3. - 2. pyYouTubeAnalysis [pyYouTubeAnalysis]_ [#]_ : This library allows one to - run searches, collect comments and define search params (search keywords, - timeframe and type). Furthermore, the project includes error handling so code - execution does not stop due to unforeseen errors while interacting with - YouTube API. Additional features include NLP methods such as SpaCy based - Named Entity Recognition (NER) that runs location extraction on text. + 2. pyYouTubeAnalysis [pyYouTubeAnalysis]_ [#]_ : This library allows the user to + run searches, collect videos and comments, and define search params (search keywords, + timeframe, and type). Furthermore, the project includes error handling that allows + code execution to continue and not stop due to unforeseen errors while interacting with + YouTube data API. Additional features included in pyYouTubeAnalysis are NLP + methods for social media text pre-processing mentioned in a later section + `Data Cleaning Techniques`, NLTK based keyphrase extraction and SpaCy based + Named Entity Recognition (NER) that runs entity extraction on text. .. [#] https://developers.google.com/youtube/v3/docs/search/list .. [#] https://developers.google.com/youtube/v3/quickstart/python @@ -214,29 +225,30 @@ as follows. Social Media / YouTube Data Noise --------------------------------- -.. figure:: comments.png - :scale: 42% - :figclass: w - - Random sample of YouTube comments representing writing style diversity. - :label:`commentsfig` - Text fields are available within several places on YouTube, including video title, description, tags, comments, channel title and channel description. Video title, -description, tags and channel title and description are filled by the +description, tags, and channel title and description are filled by the content/channel owner. Comments on the other hand are made by individuals -reacting to a video. +reacting to a video using words and language. The challenges in such a data source arise due to writing style diversity, language diversity and topic diversity. Figure :ref:`commentsfig` shows a few examples of language diversity. On social media, people use abbreviations, and sometimes these abbreviations may not be the most popular ones. Other than the -non-traditional abbreviation usage, different languages, different text lengths +non-traditional abbreviation usage, different languages, different text lengths, and emojis used by commenters are observed. + Data Cleaning Techniques ~~~~~~~~~~~~~~~~~~~~~~~~ +.. figure:: comments.png + :scale: 42% + :figclass: w + + Random sample of YouTube comments representing writing style diversity. + :label:`commentsfig` + Based on some noise seen on YouTube and other social media platforms, the following data cleaning techniques have been found to be helpful cleaning methods. @@ -244,6 +256,9 @@ following data cleaning techniques have been found to be helpful cleaning method Social media text data comes with a lot of URLs. Depending on the task at hand, removing the urls have been observed to come in handy for cleaning the text. + Remove the URLs prior to passing text through keyphrase or NER extractions has + been found to return cleaner results. This implementation is also contained in + pyYouTubeAnalysis. .. code-block:: python @@ -264,9 +279,16 @@ following data cleaning techniques have been found to be helpful cleaning method 2. Removing emojis - Emojis are widely used across social media and provide benefit in certain NLP - tasks, such as sentiment analysis. On the contrary, for many other NLP tasks, - removing emojis from text can be a useful cleaning method. + Emojis are widely used across social media by users to express emotions. + Emoijis provide benefit in some NLP tasks, such as certain sentiment analysis + implementations that rely on emoji based detections. On the contrary, for many + other NLP tasks, removing emojis from text can be a useful cleaning method that + improves the quality of the processed outcome. For named-entity recognition and + keyphrase extraction, certain emojis are observed getting falsely detected as + locations or nouns of the type NN or NNP. This impacts the quality of the NLP + methods. Removing the emojis prior to passing such text through named-entity + recognition or keyphrase extractions has been found to return cleaner results. + This implementation is also contained in pyYouTubeAnalysis. .. code-block:: python @@ -277,7 +299,6 @@ following data cleaning techniques have been found to be helpful cleaning method flags=re.UNICODE ) - def remove_emojis(txt): """ Remove emojis from input text @@ -309,7 +330,7 @@ following data cleaning techniques have been found to be helpful cleaning method Nonetheless, detection and translation is a popular technique while dealing with multiple different languages. Some examples of Python libraries that can be used for language detection - include langdetect [langdetect]_, Pycld2 [#]_, Textblob [textblob]_ and Googletrans [#]_. + include langdetect [langdetect]_, Pycld2 [#]_, Textblob [textblob]_, and Googletrans [#]_. Translate [#]_ and Googletrans can be used for language translations. .. [#] https://pypi.org/project/pycld2/ @@ -320,13 +341,6 @@ following data cleaning techniques have been found to be helpful cleaning method Trend Analysis Case Study --------------------------- -.. figure:: flights_s.png - :scale: 50% - :figclass: w - - Global flight search patterns in 2020. - :label:`flightsfigglobal` - .. figure:: flights.png :scale: 100% :figclass: w @@ -342,15 +356,26 @@ international flight searches from the US using Kayak. Right before lockdown and were enforced starting in March across different places across the globe, a big spike can be seen in flight searches, correlating with the activity of people trying to fly back home if they were elsewhere before restrictions disabled -them to do so. A massive reduction in flight searches can be seen in figures -:ref:`flightsfig` and :ref:`flightsfigglobal` [#]_ after March and as travel -was reduced due to COVID imposed events and restrictions. +them to do so. + +.. figure:: flights_s.png + :scale: 50% + :figclass: w + + Global flight search patterns in 2020. + :label:`flightsfigglobal` + +A massive reduction in flight searches can further be seen in figure +:ref:`flightsfigglobal` [#]_ showing the impact at a global level. Timeline beyond +January of 2020 for China, and beyond March of 2020 for most other locations, +faced the most impact as travel was reduced due to COVID imposed events and +restrictions. .. [#] https://www.kayak.com/news/category/travel-trends/ .. [#] https://www.sojern.com/blog/covid-19-insights-on-travel-impact-hotel-agency/ .. figure:: hotels.png - :scale: 50% + :scale: 43% :figclass: w Hotel booking search patterns in 2020. @@ -365,7 +390,7 @@ Let’s try to correlate these findings and understand content consumption withi those time periods on YouTube. First, a search was performed to gather videos about “travel vlogs” using the -pyYouTubeAnalysis [pyYouTubeAnalysis]_. Travel vlogs are a popular content +pyYouTubeAnalysis library. Travel vlogs are a popular content genre on YouTube where a lot of people are able to find reviews, advice and sneak peaks of different destinations that wows them and inspires travel plans. Such videos typically consist of people traveling to different locations @@ -389,8 +414,9 @@ and recording themselves at different spots. Statistically, it can be seen from figures :ref:`viewsyearfig`, :ref:`likesyearfig` and :ref:`commentsyearfig` that travel vlog has been a growing topic of interest and has been growing along with online content consumption over -the years up till 2019. A downward trend was seen in average views, comments and -likes on travel vlog videos in 2020. +the years up till 2019. A downward trend was seen in average views, comments, and +likes on travel vlog videos in 2020, where the views went down by 50% compared +to the year before. .. figure:: views_month.png @@ -409,6 +435,8 @@ likes on travel vlog videos in 2020. Difference in video engagements between 2019 and 2020. :label:`statsshiftfig` +To understand the differences between the travel vlog content consumed in 2019 +versus 2020 in further detail, a monthly data crawl was performed. Figures :ref:`viewsmonthfig`, :ref:`likesmonthfig` and :ref:`commentsmonthfig` show a month over month comparison between 2019 and 2020 to analyze average audience engagement patterns. The viewership trends @@ -421,7 +449,7 @@ restrictions. The chart representing "travel vlog" content engagement largely correlates with the flight search trend as shown in figure :ref:`flightsfig`. It can be seen however, people were still creating travel vlogs and commenting on such videos. Between June and September 2020, amidst a much-reduced travel, what were -these videos, what content was getting created, who was creating it and what were +these videos, what content was getting created, who was creating it, and what were the commenters talking about? .. figure:: videos.png @@ -430,12 +458,24 @@ the commenters talking about? :label:`videofig` Figure :ref:`videofig` shows a word cloud representation of what these videos -were about. Travel that would entail easier implementation of social distance -was seen popping up in 2020, such as hiking, beach trips and road traveling. +talked about generated using keyphrase extraction implementation in pyYouTubeAnalysis, +where the text passes through data cleaning techniques prior to keyphrase extraction +that is inbuilt within the implementation. Application of these techniques prior +to extracting keyphrases eliminated the noisy samples and improved the overall +results quality. Additionally, wordcloud [wordcloud]_ [#]_ was used for creating +the visualization. Word cloud is a form of term occurrence visualization where +the size of the appearance of a term in the word cloud is directly proportional +to its occurrence count. Travel that would entail easier implementation of social +distance was seen popping up in 2020, such as hiking, beach trips and road traveling. Location names such as Italy, France and Spain were also seen showing up in the videos. -YouTube influencer channels that drove high engagement during summer and fall of 2020 -include the following. +.. [#] https://pypi.org/project/wordcloud/, https://www.wordclouds.com/ + +While we have seen what content gained the most engagement, let's look into who +the creators of such content were that drove the most comments and engagement. +With the help of engagement statistics and videos read for the 2020 time frame, +the YouTube influencer channels that drove high engagement during summer and fall +of 2020 include the following. 1. 4K Walk [#]_ – YouTube channel creating videos about walking tours all over Europe and America. @@ -475,16 +515,21 @@ as follows. can start travelling again. Thanks for sharing your experience. :)" It was seen that people expressed interest in inquiring about the lifting of the -travel ban due to COVID, pre-travel COVID test requirements along with the -sentiment of waiting to travel again. People were seen mentioning a lot of -location names in their comments. With the help of SpaCy’s NER, location -extractions were performed and figure :ref:`locsfig` shows the location popularly -mentioned by commenters. One can see European locations, along with some Asian -and American locations which correlate with travel restriction reductions in -some of the places. - -This analysis, including data collection and NER, was performed -using pyYouTubeAnalysis library [pyYouTubeAnalysis]_ [#]_. +travel ban due to COVID, pre-travel COVID test requirements, along with the +sentiments around being able to travel again. People were seen mentioning a lot of +location names in their comments. With the help of named-entity recognition +implementation in pyYouTubeAnalysis, location extractions were performed. +The underlying process passed the comments through URLs and emojis removal prior +to location extraction, which led to cleaner results and reduced manual filtering. +Figure :ref:`locsfig` shows the location popularly mentioned by commenters in a +word cloud representation. One can see European locations, along with some Asian +and American locations which correlate with travel restriction reductions in some +of the places. + +This analysis, including data collection from social media, keyphrase extraction, +and NER, was performed using pyYouTubeAnalysis library [pyYouTubeAnalysis]_ [#]_. +Similar analysis for content other than "travel vlogs" can be performed for custom +time windows using similar tools and the other NLP libraries mentioned in this effort. .. [#] https://github.com/jsingh811/pyYouTubeAnalysis @@ -492,17 +537,21 @@ using pyYouTubeAnalysis library [pyYouTubeAnalysis]_ [#]_. Conclusion ---------- -Language usage on social media contains mixed writing styles, languages, freeform -emoji and abbreviation usage. This variety makes it harder to perform NLP tasks +User content creations and interactions via text on social media platforms contain +mixed writing styles, topics, languages, typing errors, freeform emojis and abbreviations. +This diversity of content and language makes it harder to perform NLP tasks on data coming from social media. Described cleaning techniques such as emoji removal, -hyperlink removal, language translations and typo corrections have been found -useful in dealing with language of such nature. +hyperlink removal, language detection and translations, and typo corrections have +been found useful in priming and pre-processing language of such nature. +Subjecting the text through these methods prior to other Natural Language +Processing (NLP) methods such as keyphrase extraction and named-entity recognition +result in cleaner output. -Social media data contain statistics in addition to text data that measure human -engagement and interest in content. Combining these statistics with inferences -from NLP techniques such as named-entity recognition (NER) are found to be helpful -in trend analysis and analytics around correlations and interests of people -engaging with social media. +Social media data contain statistics in addition to text data that measures human +engagement and interest in different types of content. Combining these statistics +with inferences from NLP techniques such as named-entity recognition (NER) and +keyphrase extraction are found to be helpful in trend analysis, analytics, and +observing correlations and affinities of user engagement with social media. References @@ -538,3 +587,11 @@ References OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016). https://www.aclweb.org/anthology/L16-1147. + +.. [rehurek2011gensim] Rehurek, R., & Sojka, P. (2011). Gensim–python framework for vector + space modelling. NLP Centre, Faculty of Informatics, Masaryk University, + Brno, Czech Republic, 3(2). + +.. [wordcloud] Oesper, L., Merico, D., Isserlin, R., & Bader, G. D. (2011). + WordCloud: a Cytoscape plugin to create a visual semantic summary of + networks. Source Code for Biology and Medicine, 6(1), 7. From 551ca59d231c9620775dea250869301aa2a0d8ac Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 15 Jun 2021 16:49:48 -0700 Subject: [PATCH 12/15] Fix image placement --- papers/jyotika_singh/jyotika_singh.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 9e91dd64ec..03654d8eaf 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -225,6 +225,13 @@ as follows. Social Media / YouTube Data Noise --------------------------------- +.. figure:: comments.png + :scale: 42% + :figclass: w + + Random sample of YouTube comments representing writing style diversity. + :label:`commentsfig` + Text fields are available within several places on YouTube, including video title, description, tags, comments, channel title and channel description. Video title, description, tags, and channel title and description are filled by the @@ -242,13 +249,6 @@ and emojis used by commenters are observed. Data Cleaning Techniques ~~~~~~~~~~~~~~~~~~~~~~~~ -.. figure:: comments.png - :scale: 42% - :figclass: w - - Random sample of YouTube comments representing writing style diversity. - :label:`commentsfig` - Based on some noise seen on YouTube and other social media platforms, the following data cleaning techniques have been found to be helpful cleaning methods. From ecfb39131b46ddecf5a588bddc317382bd0bdd55 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Tue, 29 Jun 2021 18:35:32 -0700 Subject: [PATCH 13/15] Update references --- papers/jyotika_singh/jyotika_singh.rst | 77 +++++------------ papers/jyotika_singh/paper.bib | 111 +++++++++++++++++++++++++ 2 files changed, 130 insertions(+), 58 deletions(-) create mode 100644 papers/jyotika_singh/paper.bib diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 03654d8eaf..63eecfbede 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -2,6 +2,8 @@ :email: singhjyotika811@gmail.com :institution: ICX Media, Inc. +:bibliography: paper + --------------------------------------------------- Analyzing YouTube using Python and Machine Learning --------------------------------------------------- @@ -64,14 +66,14 @@ Natural Language Processing Natural language processing (NLP) is the computer manipulation of natural language. Natural language refers to language coming from a human, either written or spoken. -[enwiki]_ defined NLP as follows: NLP is a subfield of linguistics, computer science, +:cite:`enwiki` defined NLP as follows: NLP is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The result is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. At one extreme, it could be as simple as counting word frequencies to compare different writing styles. -[bird]_ mentions, "At the other extreme, NLP involves “understanding” complete +:cite:`bird` mentions, "At the other extreme, NLP involves “understanding” complete human utterances, at least to the extent of being able to give useful responses to them. NLP is challenging because Natural language is messy. There are few rules and human language is ever evolving.". @@ -86,17 +88,18 @@ Some of the common NLP tasks on text data include the following. into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. Some popular Python libraries that can be leveraged to perform named entity - recognition for a variety of different entities include SpaCy [spacy]_ and - NLTK [bird]_. + recognition for a variety of different entities include SpaCy :cite:`spacy` and + NLTK :cite:`bird`. 2. Keyphrase extraction Keyphrase extraction is the task of automatically selecting a small set of - phrases that best describe a given free text document. [CoNLL]_ + phrases that best describe a given free text document. :cite:`CoNLL` Some popular tools that can be used for keyphrase extraction as mentioned in - this article [#]_ include Gensim [rehurek2011gensim]_ and RAKE-NLTK[#]_. - Another way keyphrase extraction can be performed is using NLTK [bird]_ methods. - This implementation is included in the pyYouTubeAnalysis [pyYouTubeAnalysis]_ library. + this article [#]_ include Gensim :cite:`rehurek2011gensim` and RAKE-NLTK[#]_. + Another way keyphrase extraction can be performed is using NLTK :cite:`bird` methods. + This implementation is included in the pyYouTubeAnalysis :cite:`pyYouTubeAnalysis` + library. .. [#] https://towardsdatascience.com/extracting-keyphrases-from-text-rake-and-gensim-in-python-eefd0fad582f .. [#] https://pypi.org/project/rake-nltk/ @@ -207,7 +210,7 @@ as follows. 1. Wrappers of YouTube Data API [#]_ : Libraries that act as wrappers and provide a way to use YouTube Data API V3. - 2. pyYouTubeAnalysis [pyYouTubeAnalysis]_ [#]_ : This library allows the user to + 2. pyYouTubeAnalysis :cite `pyYouTubeAnalysis` [#]_ : This library allows the user to run searches, collect videos and comments, and define search params (search keywords, timeframe, and type). Furthermore, the project includes error handling that allows code execution to continue and not stop due to unforeseen errors while interacting with @@ -312,8 +315,8 @@ following data cleaning techniques have been found to be helpful cleaning method Some NLP models tend to do very well for a particular style of language and word usage. On social media, the language seen can be accompanied with various incorrectly spelled words, also known as typos. - PySpellChecker [OpenSubtitles2016]_ [#]_, Autocorrect [#]_ and Textblob - [textblob]_ are examples of open-source tools that can be used for spelling + PySpellChecker :cite:`OpenSubtitles2016` [#]_, Autocorrect [#]_ and Textblob + :cite:`textblob` are examples of open-source tools that can be used for spelling corrections. .. [#] https://pypi.org/project/pyspellchecker/ @@ -330,8 +333,9 @@ following data cleaning techniques have been found to be helpful cleaning method Nonetheless, detection and translation is a popular technique while dealing with multiple different languages. Some examples of Python libraries that can be used for language detection - include langdetect [langdetect]_, Pycld2 [#]_, Textblob [textblob]_, and Googletrans [#]_. - Translate [#]_ and Googletrans can be used for language translations. + include langdetect :cite:`langdetect`, Pycld2 [#]_, Textblob :cite:`textblob`, + and Googletrans [#]_. Translate [#]_ and Googletrans can be used for language + translations. .. [#] https://pypi.org/project/pycld2/ .. [#] https://pypi.org/project/googletrans/ @@ -462,7 +466,7 @@ talked about generated using keyphrase extraction implementation in pyYouTubeAna where the text passes through data cleaning techniques prior to keyphrase extraction that is inbuilt within the implementation. Application of these techniques prior to extracting keyphrases eliminated the noisy samples and improved the overall -results quality. Additionally, wordcloud [wordcloud]_ [#]_ was used for creating +results quality. Additionally, wordcloud :cite:`wordcloud` [#]_ was used for creating the visualization. Word cloud is a form of term occurrence visualization where the size of the appearance of a term in the word cloud is directly proportional to its occurrence count. Travel that would entail easier implementation of social @@ -527,7 +531,7 @@ and American locations which correlate with travel restriction reductions in som of the places. This analysis, including data collection from social media, keyphrase extraction, -and NER, was performed using pyYouTubeAnalysis library [pyYouTubeAnalysis]_ [#]_. +and NER, was performed using pyYouTubeAnalysis library :cite:`pyYouTubeAnalysis` [#]_. Similar analysis for content other than "travel vlogs" can be performed for custom time windows using similar tools and the other NLP libraries mentioned in this effort. @@ -552,46 +556,3 @@ engagement and interest in different types of content. Combining these statistic with inferences from NLP techniques such as named-entity recognition (NER) and keyphrase extraction are found to be helpful in trend analysis, analytics, and observing correlations and affinities of user engagement with social media. - - -References ----------- -.. [enwiki] Wikipedia contributors. "Natural language processing." Wikipedia, - The Free Encyclopedia. Wikipedia, The Free Encyclopedia, - 21 May. 2021. Web. 27 May. 2021. - -.. [bird] Bird, Steven, et al. Natural Language Processing with Python. - O’Reilly Media, 2009. - -.. [CoNLL] Bennani-Smires, Kamil & Musat, Claudiu & Hossmann, Andreea & Baeriswyl, - Michael & Jaggi, Martin. (2018). Simple Unsupervised Keyphrase Extraction - using Sentence Embeddings. 10.18653/v1/K18-1022 - -.. [spacy] Honnibal, Matthew and Montani, Ines and Van Landeghem, Sofie and Boyd, Adriane. - (2020). spaCy: Industrial-strength Natural Language Processing in Python. - Zenodo. 10.5281/zenodo.1212303. https://doi.org/10.5281/zenodo.1212303 - -.. [pyYouTubeAnalysis] Singh, J. (2021). jsingh811/pyYouTubeAnalysis: - pyYouTubeAnalysis: YouTube data requests and NER on text (v1.0) - [Computer software]. Zenodo. 10.5281/ZENODO.4915746. - https://doi.org/10.5281/ZENODO.4915746 - -.. [textblob] Loria, Steven. (2018). textblob Documentation. Release 0.15, 2. - https://textblob.readthedocs.io/en/dev/ - -.. [langdetect] Shuyo, Nakatani. (2010). Language Detection Library for Java. - http://code.google.com/p/language-detection/, - https://pypi.org/project/langdetect/ - -.. [OpenSubtitles2016] P Lison, J Tiedemann. (2016). - OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. - In Proceedings of the 10th International Conference on Language Resources and Evaluation - (LREC 2016). https://www.aclweb.org/anthology/L16-1147. - -.. [rehurek2011gensim] Rehurek, R., & Sojka, P. (2011). Gensim–python framework for vector - space modelling. NLP Centre, Faculty of Informatics, Masaryk University, - Brno, Czech Republic, 3(2). - -.. [wordcloud] Oesper, L., Merico, D., Isserlin, R., & Bader, G. D. (2011). - WordCloud: a Cytoscape plugin to create a visual semantic summary of - networks. Source Code for Biology and Medicine, 6(1), 7. diff --git a/papers/jyotika_singh/paper.bib b/papers/jyotika_singh/paper.bib new file mode 100644 index 0000000000..ff2ad324d1 --- /dev/null +++ b/papers/jyotika_singh/paper.bib @@ -0,0 +1,111 @@ +@misc{enwiki, + author = "{Wikipedia contributors}", + title = "Natural language processing --- {Wikipedia}{,} The Free Encyclopedia", + year = "2021", + howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=1030186679}", + note = "[Online; accessed 25-June-2021]" +} + +@book{bird, + author = {Bird, Steven and Klein, Ewan and Loper, Edward}, + year = {2009}, + month = {01}, + pages = {}, + title = {Natural Language Processing with Python}, + isbn = {978-0-596-51649-9} +} + +@inproceedings{CoNLL, + author = {Bennani-Smires, Kamil and Musat, Claudiu and Hossmann, Andreea and Baeriswyl, Michael and Jaggi, Martin}, + year = {2018}, + month = {10}, + pages = {}, + title = {Simple Unsupervised Keyphrase Extraction using Sentence Embeddings}, + doi = {10.18653/v1/K18-1022} +} + +@software{spacy, + author = {Honnibal, Matthew and Montani, Ines and Van Landeghem, Sofie and Boyd, Adriane}, + title = {{spaCy: Industrial-strength Natural Language Processing in Python}}, + year = 2020, + publisher = {Zenodo}, + doi = {10.5281/zenodo.1212303}, + url = {https://doi.org/10.5281/zenodo.1212303} +} + +@software{pyYouTubeAnalysis, + doi = {10.5281/ZENODO.5044556}, + url = {https://zenodo.org/record/5044556}, + author = {Singh, Jyotika}, + title = {jsingh811/pyYouTubeAnalysis: YouTube Data Requests and Natural Language Processing on Text}, + publisher = {Zenodo}, + year = {2021}, + copyright = {Open Access} +} + +@article{textblob, + title={textblob Documentation}, + author={Loria, Steven}, + journal={Release 0.15}, + volume={2}, + year={2018} +} + +@article{langdetect, + added-at = {2016-10-10T16:03:01.000+0200}, + author = {Shuyo, Nakatani}, + biburl = {https://www.bibsonomy.org/bibtex/20f33a503dff8a254a64e4f38517b8621/l.sz.}, + interhash = {319b16db10ac28fa415d202c781b1530}, + intrahash = {0f33a503dff8a254a64e4f38517b8621}, + keywords = {google java langdetect python}, + timestamp = {2017-03-29T14:43:14.000+0200}, + title = {Language Detection Library for Java}, + url = {http://code.google.com/p/language-detection/}, + year = 2010 +} + +@inproceedings{OpenSubtitles2016, + author = {Pierre Lison and + J{\"{o}}rg Tiedemann}, + editor = {Nicoletta Calzolari and + Khalid Choukri and + Thierry Declerck and + Sara Goggi and + Marko Grobelnik and + Bente Maegaard and + Joseph Mariani and + H{\'{e}}l{\`{e}}ne Mazo and + Asunci{\'{o}}n Moreno and + Jan Odijk and + Stelios Piperidis}, + title = {OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and + {TV} Subtitles}, + booktitle = {Proceedings of the Tenth International Conference on Language Resources + and Evaluation {LREC} 2016, Portoro{\v{z}}, Slovenia, May 23-28, 2016}, + publisher = {European Language Resources Association {(ELRA)}}, + year = {2016}, + url = {http://www.lrec-conf.org/proceedings/lrec2016/summaries/947.html}, + timestamp = {Mon, 19 Aug 2019 15:22:18 +0200}, + biburl = {https://dblp.org/rec/conf/lrec/LisonT16.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{rehurek2011gensim, + title={Gensim--python framework for vector space modelling}, + author={Rehurek, Radim and Sojka, Petr}, + journal={NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic}, + volume={3}, + number={2}, + year={2011} +} + +@article{wordcloud, + author = {Oesper, Layla and Merico, Daniele and Isserlin, Ruth and Bader, Gary}, + year = {2011}, + month = {04}, + pages = {7}, + title = {WordCloud: A Cytoscape plugin to create a visual semantic summary of networks}, + volume = {6}, + journal = {Source code for biology and medicine}, + doi = {10.1186/1751-0473-6-7} +} From b15d1709aa8245274374f6e92a709dfb98f98022 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Wed, 30 Jun 2021 11:09:20 -0700 Subject: [PATCH 14/15] Add orcid --- papers/jyotika_singh/jyotika_singh.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 63eecfbede..121e5baa05 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -1,6 +1,7 @@ :author: Jyotika Singh :email: singhjyotika811@gmail.com :institution: ICX Media, Inc. +:orcid: 0000-0002-5442-3004 :bibliography: paper From 3db09f43b9280659376067586b910f2c595a3220 Mon Sep 17 00:00:00 2001 From: Jyotika Singh Date: Wed, 30 Jun 2021 17:53:25 -0700 Subject: [PATCH 15/15] Edit title --- papers/jyotika_singh/jyotika_singh.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/papers/jyotika_singh/jyotika_singh.rst b/papers/jyotika_singh/jyotika_singh.rst index 121e5baa05..ab8c2e9072 100644 --- a/papers/jyotika_singh/jyotika_singh.rst +++ b/papers/jyotika_singh/jyotika_singh.rst @@ -5,9 +5,9 @@ :bibliography: paper ---------------------------------------------------- -Analyzing YouTube using Python and Machine Learning ---------------------------------------------------- +------------------------------------------------------------------ +Social Media Analysis using Natural Language Processing Techniques +------------------------------------------------------------------ .. class:: abstract

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