Predicting the volume of comments on online news stories
Proceedings of the 18th ACM conference on Information and knowledge management
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Movie reviews and revenues: an experiment in text regression
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
News comments: exploring, modeling, and online prediction
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Microblog language identification: overcoming the limitations of short, unedited and idiomatic text
Language Resources and Evaluation
Exploiting user comments for audio-visual content indexing and retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Pessimists and optimists: Improving collaborative filtering through sentiment analysis
Expert Systems with Applications: An International Journal
Box office prediction based on microblog
Expert Systems with Applications: An International Journal
Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain
International Journal on Semantic Web & Information Systems
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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.