Basic sentiment analysis of comments on a youtube video using a builtin python package "Vader Lexicon Sentiment Analyser" and "TextBlob Sentiment Analyser".
I have simply used "Youtube Data API" which is available on "Google Developers Console" to scrap youtube comments of a particular video. Then I have made use of python library called "NLTK", a platform for building python programs to work with Human language data. More specifically, what I have used is called VADER (Valence Aware Dictionary and Sentiment Reasoner) which is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed on social media. I have also used TextBlob Sentiment Analyser and compared it with vader lexicon to give a machine generated report on sentiments of comments that are posted (Expressed) on a particular video.
The basic idea behind sentiment analysis using vader lexicon and TextBlob is that it contains a dictionary of words with some value assigned to it. Eg a word like "Good" or "Amazing" would have some Positive value assigned to it and a word like "Bad" or "sad" would have a Negative value assigned to it. So what I did is that I made a program that reads all the comments on a particular youtube video and then calculate Compound Score for each line and label it according to the following relation:-
Weakly Positive sentiment: compound score <= 0.3Positive sentiment: compound score >= 0.3 and <= 0.6Strongly Positive sentiment: compound score >= 0.6Neutral sentiment: compound score = 0Weakly Negative sentiment: compound score <= 0 and > -0.3Negative sentiment: compound score <= -0.3 and > -0.6Strongly Negative sentiment: compound score <= -0.6
You will have to install some libraries. I have provided requirements.txt file so open command prompt and Run:
pip install -r requirements.txt
Running the program
First of all enter your developer key in the code where it is written #PutYourKeyHere. Then type in your terminal python youtube_comments_sentiment_analysis.py.
