Abstract
In this paper, we investigate how discovering the topic dicussed in a tweet can be used to improve its sentiment classification. In particular, a classifier is introduced consisting of a topic-specific classifier, which is only trained on tweets of the same topic of the given tweet, and a generic classifier, which is trained on all the tweets in the training set. The set of considered topics is obtained by clustering the hashtags that occur in the training set. A classifier is then used to estimate the topic of a previously unseen tweet. Experimental results based on a public Twitter dataset show that considering topic-specific sentiment classifiers indeed leads to an improvement.
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Van Canneyt, S., Claeys, N., Dhoedt, B. (2015). Topic-Dependent Sentiment Classification on Twitter. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_48
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DOI: https://doi.org/10.1007/978-3-319-16354-3_48
Publisher Name: Springer, Cham
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