Predicting IMDB movie ratings using social media

We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extrac...

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Bibliographic Details
Published inAdvances in Information Retrieval pp. 503 - 507
Main Authors Oghina, Andrei, Breuss, Mathias, Tsagkias, Manos, de Rijke, Maarten
Format Conference Proceeding Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer-Verlag 01.04.2012
Springer Berlin Heidelberg
SeriesACM Other Conferences
Subjects
Activity Indicator
Attribute Selection
Mean Absolute Error
Social Medium
Textual Feature
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Summary:We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data from either of these channels can help to extract a better set of textual feature for prediction. Our best performing model is able to rate movies very close to the observed values.
ISBN:9783642289965
3642289967
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-28997-2_51