Abstract
Sentiment classification is the most active field in opinion mining that aims to determine whether an opinionated text expresses a positive, negative or neutral opinion. Existing lexicon based sentiment classification methods are unable to deal with context or domain-specific words. To solve this problem, Word Senses Disambiguation (WSD) is useful to identify the most related meaning (sense) of a word in a sentence. In this paper, a sense level sentiment classification method is proposed that determine the sentiment polarity of words using graph based WSD algorithm and a multiple meaning (sense) sentiment lexicon. To evaluate the impact of WSD on sentiment classification, the proposed method compared against a baseline method using two subjectivity lexicons, namely the MPQA and SentiWordNet. Experimental results using a benchmark dataset show that the WSD is effective for sentiment classification.
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Jalilvand, A., Salim, N. (2012). Sentiment Classification Using Graph Based Word Sense Disambigution. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_35
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DOI: https://doi.org/10.1007/978-3-642-35326-0_35
Publisher Name: Springer, Berlin, Heidelberg
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