Movie Rating Prediction Based on Twitter Sentiment Analysis - Kesharwani, Abhishek
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With microblogging platforms such as Twitter generating huge amounts of textual data every day, the possibilities of knowledge discovery through Twitter data becomes increasingly relevant. Similar to the public voting mechanismnon websites such as the Internet Movie Database (IMDb) that aggregates movies ratings, Twitter content contains reflections of public opinion about movies. This study aims to explore the use of Twitter content as textual data for predicting the movie rating. In this study, we extract number of tweets and compiled to predict the rating scores of newly released movies.…mehr

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Produktbeschreibung
With microblogging platforms such as Twitter generating huge amounts of textual data every day, the possibilities of knowledge discovery through Twitter data becomes increasingly relevant. Similar to the public voting mechanismnon websites such as the Internet Movie Database (IMDb) that aggregates movies ratings, Twitter content contains reflections of public opinion about movies. This study aims to explore the use of Twitter content as textual data for predicting the movie rating. In this study, we extract number of tweets and compiled to predict the rating scores of newly released movies. Predictions were done with the algorithms, exploring the tweet polarity. In addition, this study explores the use of several different kinds of tweet classification Algorithm and movie rating algorithm. Results show that movie rating developed by our application is compared to IMDB and Rotten Tomatoes.
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Autorenporträt
Mr. Abhishek Kesharwani ,working as Assistant Professor at United College of Engineering and Research, Computer Science and Engineering dept.This book aims to explore the use of Twitter content as textual data for predicting the movie rating, explain methods of tweet extraction, predicting sentiment of tweets and algorithm for tweet classification.