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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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 68 pp. Englisch. N° de réf. du vendeur 9786202051538
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock. N° de réf. du vendeur zk6202051531
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kesharwani AbhishekMr. 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 . N° de réf. du vendeur 174674898
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch. N° de réf. du vendeur 9786202051538
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9786202051538
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Movie Rating Prediction Based on Twitter Sentiment Analysis | Twitter Sentiment Analysis | Abhishek Kesharwani | Taschenbuch | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202051538 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 113378943
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