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Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain

Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain

Andrés García-Silva, Víctor Rodríguez-Doncel, Oscar Corch
Copyright: © 2013 |Volume: 9 |Issue: 3 |Pages: 13
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466633438|DOI: 10.4018/ijswis.2013070101
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MLA

García-Silva, Andrés, et al. "Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain." IJSWIS vol.9, no.3 2013: pp.1-13. http://doi.org/10.4018/ijswis.2013070101

APA

García-Silva, A., Rodríguez-Doncel, V., & Corch, O. (2013). Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain. International Journal on Semantic Web and Information Systems (IJSWIS), 9(3), 1-13. http://doi.org/10.4018/ijswis.2013070101

Chicago

García-Silva, Andrés, Víctor Rodríguez-Doncel, and Oscar Corch. "Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain," International Journal on Semantic Web and Information Systems (IJSWIS) 9, no.3: 1-13. http://doi.org/10.4018/ijswis.2013070101

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Abstract

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.

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