Twitter Data Analysis: Use Twitter data for sentiment analysis. Identify the tweets which are hate tweets and which are not.
This program is written in Python3 and using (Tweepy, TextBlob) library to do analysis.
It is often argued, if machines can understand emotions or sentiments like humans do.
...Is it possible to build a programme, which can justify the sentiments just by analysing a piece of text?
...Is it possible to know whether the content at hand is positive, negative or neutral? Yes it is!
A method of Opinion Mining, often referred to as Sentiment Analysis to extract sentiments by using classification algorithms. Sentiment analysis refers to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, i.e., whether the attitude behind this text is positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great help for commercial and political use, among others. The task can be conducted on different levels, classifying the polarity of words, sentences or entire documents.
In our approach, Tweepy API for python was used to extract tweets from Twitter Database. The basis of extraction were hashtags, which were the major highlight of twitter, the microblogging website. Using Tweepy and polarity based algorithms; we were able to actually define, the positive, negative and neutral attributes of the text, and generate a percentage wise breakup, hence performing sentiment analysis.
Step 1: Install Python
- Check if python is already installed on your system :
$python –version - If Python 2.7 or later is not installed, install Python with your distribution's package manager. The command and package name varies
$sudo apt-get install python3 - Check if python is now installed on your system:
$python3 –version
Step 2: Installing API’s \
- Tweepy -
$sudo pip-install tweepy
Step 3: Run the program
- git the repository
- run
$python twitter.py
The Google Natural Language API is an easy to use interface to a set of powerful NLP models which have been pre-trained by Google to perform various tasks. The biggest advantage of using these pre-trained models via the API is, that no training dataset is needed. The API allows the user to immediately start making predictions, which can be very valuable in situations where little labeled data is available.
The Natural Language API comprises five different services:
- Syntax Analysis
- Sentiment Analysis
- Entity Analysis
- Entity Sentiment Analysis
- Text Classification
Google’s sentiment analysis provide the prevailing emotional opinion within a provided text. The API returns two values: The “score” describes the emotional leaning of the text from -1 (negative) to +1 (positive), with 0 being neutral.
The “magnitude” measures the strength of the emotion.
I randomly selected 500 positive and 500 negative movie reviews from the test set and compared the predicted sentiment to the actual review label. The confusion matrix and the table shows, the model is right about 94% of the time for good and bad movie reviews.