›› 2012, Vol. 27 ›› Issue (3): 650-666.doi: 10.1007/s11390-012-1251-y

• Special Issue on Social Network Mining • Previous Articles    

Phrase-Level Sentiment Polarity Classification Using Rule-Based Typed Dependencies and Additional Complex Phrases Consideration

Luke Kien-Weng Tan1 (陈坚永), Jin-Cheon Na1 (罗镇川), Member, ACM, Yin-Leng Theng1 (邓燕玲), and Kuiyu Chang2 (张圭煜)   

  1. 1. Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link 637718, Singapore;
    2. School of Computer Engineering, Nanyang Technological University, Block N4 Nanyang Avenue, 639798, Singapore
  • Received:2011-09-01 Revised:2012-01-19 Online:2012-05-05 Published:2012-05-05
  • About author:Luke Kien-Weng Tan received the B.Eng degree from National Uni-versity of Singapore (NUS), and the M.S. degree in information system from Nanyang Technological Univer-sity (NTU), Singapore. Currently, he is a Ph.D. candidate in the Wee Kim Wee School of Communication & In-formation at NTU.

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.

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