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|Title:||Phrase-level sentiment polarity classification using rule-based typed dependencies and additional complex phrases consideration||Authors:||Na, Jin-Cheon
Tan, Luke Kien-Weng
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||Source:||Tan, L. K.-W., Na, J.-C., Theng, Y.-L., & Chang, K. (2012). Phrase-level sentiment polarity classification using rule-based typed dependencies and additional complex phrases consideration. Journal of computer science and technology, 27(3), 650-666.||Series/Report no.:||Journal of computer science and technology||Abstract:||The advent of Web 2.0 has led to an increase in user-generated content on the Web. This has provided an extensive collection of free-style texts with opinion expressions that could influence the decisions and actions of their readers. Providers of such content exert a certain level of influence on the receivers and this is evident from blog sites having effect on their readers’ purchase decisions, political view points, financial planning, and others. By detecting the opinion expressed, we can identify the sentiments on the topics discussed and the influence exerted on the readers. In this paper, we introduce an automatic approach in deriving polarity pattern rules to detect sentiment polarity at the phrase level, and in addition consider the effects of the more complex relationships found between words in sentiment polarity classification. Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing, providing a refined analysis on the grammar and semantics of textual data. Heuristics are typically used to determine the typed dependency polarity patterns, which may not comprehensively identify all possible rules. We study the use of class sequential rules (CSRs) to automatically learn the typed dependency patterns, and benchmark the performance of CSR against a heuristic method. Preliminary results show CSR leads to further improvements in classification performance achieving over 80% F1 scores in the test cases. In addition, we observe more complex relationships between words that could influence phrase sentiment polarity, and further discuss on possible approaches to handle the effects of these complex relationships.||URI:||https://hdl.handle.net/10356/107176
|ISSN:||1000-9000||DOI:||10.1007/s11390-012-1251-y||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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