Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Using Model Trees for Classification
Machine Learning
The Journal of Machine Learning Research
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Analyzing entities and topics in news articles using statistical topic models
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Predicting IMDB movie ratings using social media
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Towards context-aware search and analysis on social media data
Proceedings of the 16th International Conference on Extending Database Technology
Hierarchical Classification Approach to Emotion Recognition in Twitter
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02
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In the entertainment domain users tweet about their expectations and opinions regarding upcoming, current and past experiences, while companies advertise and promote the shows. This characterization, important for customers and companies, goes beyond traditional sentiment analysis where the polarity of the sentiments expressed in opinions is usually identified as positive, negative or neutral. The authors investigate different tweet representation models, including bags of words and probabilistic topic models, to shed light on the semantics of the messages. Their experiments show that topic-based models generated with Latent Dirichlet Allocation LDA yield, most of the times, better categorizations when compared to TF-IDF based features, particularly when these models are enriched with natural language features and specific Twitter slang.