Sentiment Analysis is one of the Natural Language Processing techniques, which can be used to determine the sensibility behind the texts, i.e. tweets, movie reviews, YouTube comments, any incoming message, etc. It’s in use on a large scale as many big firms are using it to examine customer reviews about their product/services, on social media, their websites which helps them to maintain their brand values. Some of the big corporations using this technique to invigorate customer engagement with their service areas:
• Trip Advisor • Google • Apple • KFC
Other application of sentiment analysis includes:
• Twitter sentiment analysis, • IMDB movie ratings, • Amazon customer reviews, • YouTube videos comments
Current YouTube usage statistics indicate the approximate scale of the site: at the time of this writing there are quite 1 billion unique users viewing video content, watching over 6 billion hours of video each month. Also, YouTube accounts for 20% of web traffic and 10% of total internet traffic. YouTube provides many social mechanisms to gauge user opinion and views a few videos by means of voting, rating, favourites, sharing and negative comments, etc. It’s important to notice that YouTube provides more than just video sharing
The Project is based on Natural language processing technique We have done sentimental analysis of public comments by using 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. Calculating the sentiment scores of positive, Negative, and neutral comments, analysing YouTube channels with 100% positive comments and 0% positive comments and forming Wordcloud of most frequent , positive negative and neutral comments.
Dataset contains 2 csv files named as USvideos and UScomments both containing string and int datatypes and are totally different from one another. Dataset has been collected / downloaded from Kaggle. More about dataset:
- USvideos: Contains data in 11 sections i.e. a. Video_id b. Title c. Channel_title d. category_id e. tags f. views g. likes h. dislikes i. comments j. thumbnail k. date
- USvideos: Contains data in 4 sections i.e. a. Video_id b. Comment_text c. Likes d. replies
This project predicts sentiment analysis system for you-tube comments. The basic idea behind sentiment analysis using Vader lexicon. We have analysed 305358 positive sentiments, 260986 neutral and 25030 negative sentiments,Channels with their positive percentage number.We have also scrutinized the channels having 100% positive sentiments and channels with 0% positive comments. Word cloud of most frequent, positive, neutral and negative comments