Predicting IMDB movie ratings using social media
| Authors |
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| Publication date | 2012 |
| Host editors |
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| Book title | Advances in Information Retrieval |
| Book subtitle | 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012: proceedings |
| ISBN |
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| ISBN (electronic) |
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| Series | Lecture Notes in Computer Science |
| Event | 34th European Conference on Information Retrieval (ECIR 2012) |
| Pages (from-to) | 503-507 |
| Publisher | Heidelberg: Springer |
| Organisations |
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| Abstract | 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. |
| Document type | Conference contribution |
| Language | English |
| Published at |
https://doi.org/10.1007/978-3-642-28997-2_51
(Final published version)
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