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) |
Abstract
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 |
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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 |
URI: | https://orca.cardiff.ac.uk/id/eprint/65403 |
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