• Neha Moyal
• Ruchika Honawale
• Shital Talekar
• Vaishnavi Sadar
• Prof. Sunita Patil

The activity of Social-TV viewers has considerably grown in last few years. The web TV Show Recommendation based on sentiment analysis is quite interesting and extremely challenging. Users can be able to add ratings, suggest, comment them and review on TV shows. Web has been socially empowering many audiences in unique ways. Now days the rapid development of sharing websites, so many people would like to be audiences in their daily entertainments such as TV shows and websites, to attract major audiences many efforts have been taken for popularity prediction. Different episodes are released on different days to predict the popularity for TV shows are challenging so we introduce this scheme that predict the TV shows based on sentiment analysis. Sentiment Analysis algorithms offers social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. The uses of assumption examination have capacity to separate bits of knowledge from social information is a training that is in effect generally embraced by associations across world. The aim of this work is to gauge the performance of TV program and conjointly calculate what number folks are likeable to a specific show or actors of that show and predicting quality of that shows, supported the text reviews. we tend to are becoming reviews on social networking websites.

TV show recommendation, Sentiment analysis, Social application, Review analysis

"SURVEY PAPER ON TV SHOW POPULARITY PREDICTION USING SENTIMENT ANALYSIS IN SOCIAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.a19-a23, June-2021, Available :http://www.jetir.org/papers/JETIR2106003.pdf

"SURVEY PAPER ON TV SHOW POPULARITY PREDICTION USING SENTIMENT ANALYSIS IN SOCIAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppa19-a23, June-2021, Available at : http://www.jetir.org/papers/JETIR2106003.pdf