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

• Published in: Journal of computer science and technology Vol. 27; no. 3; pp. 650 - 666
• Main Author: 陈坚永 罗镇川 邓燕玲 张圭煜
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.01.2012; Springer Nature B.V; Wee Kim Wee School of Communication and Information,Nanyang Technological University,31 Nanyang Link 637718,Singapore%School of Computer Engineering,Nanyang Technological University,Block N4 Nanyang Avenue,639798,Singapore
• Subjects: Artificial Intelligence; Benchmarking; Blogs; Classification; Computer Science; Data mining; Data Structures and Information Theory; Decisions; Financial planning; Heuristic methods; Information Systems Applications (incl.Internet); Oil spills; Polarity; Readers; Regular Paper; Semantics; Sentiment analysis; Software Engineering; Texts; Theory of Computation; User generated content; 依赖性; 分类; 基础; 情绪; 极性; 短语; 类型; 词组; typed dependency; complex phrase; class sequential rule; sentiment analysis
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-012-1251-y

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 eases. 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.