A New Sentiment Analysis based Application for Analyzing Reviews of Web Series and Movies of Different Genres
Aishwarya, Parth Wadhwa, Prabhishek Singh
Abstract
This research paper proposes an application of sentiment analysis that works on the principle of machine learning. The proposed application provides a comparative analysis of web series and movies of different genres of a particular time period on the basis of sentiments of the viewers. Data is fetched from twitter through API keys and twitter access tokens. The movies and web series from the year 2017 to 2019 of four different genres were taken and sentiment analysis was performed on each web series and movie, which gives result in the form of positive reviews and negative reviews. The famous hashtag for each movie and web series are determined. The total number of tweet counts is 3000. A Table of each genre was formed that contained the name of movie and web series, percentage of positive sentiments of corresponding web series or movie and percentage of negative sentiments of corresponding movie or web series. The graphical representation of each genre was done to analyze the results graphically. The combined analysis was performed after calculating the average percentage reviews of a positive and negative sentiment of all the movies and web series of each genre. The graphical representation of the combined analysis is done to analyze the final results. Through the proposed application results were analyzed concluding that whether movies or web series of a particular genre in the year 2017-19 were more liked by the viewers.