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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,205 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import nltk\n", | ||
| "import csv\n", | ||
| "import numpy as np" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "negative = []\n", | ||
| "with open('words_negative.csv', newline='') as file:\n", | ||
| " reader = csv.reader(file)\n", | ||
| " for row in reader:\n", | ||
| " negative.append(row)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "positive = []\n", | ||
| "with open(\"words_positive.csv\", newline='') as file:\n", | ||
| " reader = csv.reader(file)\n", | ||
| " for row in reader:\n", | ||
| " positive.append(row)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def sentiment(text):\n", | ||
| " temp = []\n", | ||
| " text_sent = nltk.sent_tokenize(text)\n", | ||
| " for sentence in text_sent:\n", | ||
| " n_count = 0\n", | ||
| " p_count = 0\n", | ||
| " sent_words = nltk.word_tokenize(sentence)\n", | ||
| " for word in sent_words:\n", | ||
| " print (word)\n", | ||
| " for item in positive:\n", | ||
| " if(word == item[0]):\n", | ||
| " print(\"---> \"+item[0])\n", | ||
| " p_count +=1\n", | ||
| " for item in negative:\n", | ||
| " if(word == item[0]):\n", | ||
| " print(\"---> \"+item[0])\n", | ||
| " n_count +=1\n", | ||
| "\n", | ||
| " if(p_count > 0 and n_count == 0): #any number of only positives (+) [case 1]\n", | ||
| " #print \"+ : \" + sentence\n", | ||
| " temp.append(1)\n", | ||
| " elif(n_count%2 > 0): #odd number of negatives (-) [case2]\n", | ||
| " #print \"- : \" + sentence\n", | ||
| " temp.append(-1)\n", | ||
| " elif(n_count%2 ==0 and n_count > 0): #even number of negatives (+) [case3]\n", | ||
| " #print \"+ : \" + sentence\n", | ||
| " temp.append(1)\n", | ||
| " else:\n", | ||
| " #print \"? : \" + sentence\n", | ||
| " temp.append(0)\n", | ||
| " return temp" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "sentiment(\"It was terribly bad.\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "sentiment(\"Actualluty, it was not bad at all.\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "sentiment(\"This is a sentance about nothing.\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "mylist = sentiment(\"I saw this movie the other night. I can say I was not disappointed! The actiing and story line was amazing and kept me on the edge of my seat the entire time. While I did not care for the music, it did not take away from the overall experience. I would highly recommend this movie to anyone who enjoys thirllers.\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "mylist" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "comments = []\n", | ||
| "with open(\"reviews.csv\", newline='') as file:\n", | ||
| " reader = csv.reader(file)\n", | ||
| " for row in reader:\n", | ||
| " comments.append(row)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "comments[1]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "rate = sentiment(str(comments[0]))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "np.average(rate)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "for review in comments:\n", | ||
| " print(\"\\n\")\n", | ||
| " print(np.average(sentiment(str(review))))\n", | ||
| " print(review)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.6.5" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |