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R U :-) or :-( ? Character- vs. word-gram feature selection for sentiment classification of OSN corpora

Blamey, Benjamin, Crick, Tom and Oatley, Giles 2012. R U :-) or :-( ? Character- vs. word-gram feature selection for sentiment classification of OSN corpora. Research and Development in Intelligent Systems XXIXL Incorporating Applications and Innovations in Intelligent Systems XX Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Inte, London: Springer, pp. 207-212. (10.1007/978-1-4471-4739-8_16)

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Binary sentiment classification, or sentiment analysis, is the task of computing the sentiment of a document, i.e. whether it contains broadly positive or negative opinions. The topic is well-studied, and the intuitive approach of using words as classification features is the basis of most techniques documented in the literature. The alternative character n-gram language model has been applied successfully to a range of NLP tasks, but its effectiveness at sentiment classification seems to be under-investigated, and results are mixed. We present an investigation of the application of the character n-gram model to text classification of corpora from online social networks, the first such documented study, where text is known to be rich in so-called unnatural language, also introducing a novel corpus of Facebook photo comments. Despite hoping that the flexibility of the character n-gram approach would be well-suited to unnatural language phenomenon, we find little improvement over the baseline algorithms employing the word n-gram language model.

Item Type: Book Section
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer
ISBN: 9781447147381
Date of Acceptance: 9 October 2012
Last Modified: 11 Nov 2016 09:15

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