Robust feature extraction method for automatic sentiment classification of erroneous online customer reviews

초록

In morpheme-based languages such as Korean, Japanese, Turkish, and Hungarian, spacing and spelling errors that frequently occur in online documents make it difficult to reliably extract informative lexical clues for sentiment analysis, a well-known natural language application. To overcome this problem, we propose a simple, reliable lexical feature extraction method for sentiment classification systems; this method targets online customer reviews in Korean, which include numerous spacing and spelling errors. The proposed method performs longest-matching between input sentences and two kinds of patterns (spacing-unit patterns and phoneme patterns) that are automatically constructed from a large POS tagged corpus. Thereafter, the method returns content words associated with the longest matched patterns. In the experiments on sentiment classification, the proposed method outperformed previous lexical feature extraction methods, which are based on conventional morphological analyzers. ©2013 International Information Institute.

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