Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments

Abstract

Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%.