›› 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.

[1] Adar E, Adamic L A. Tracking information epidemics inblogspace. In Proc. Int. Conf. Web Intelligence, Wash-ington, DC, USA, Sept. 2005, pp.207-214.

[2] Agarwal N, Liu H, Tang L, Yu P S. Identifying the influentialbloggers in a community. In Proc. WSDM 2008, New York,USA, Feb. 2008, pp.207-218.

[3] Tan L K W, Na J C, Theng Y L. Influence detection be-tween blog posts through blog features, content analysis, andcommunity identity. Online Information Review, 2011, 35(3):425-442.

[4] Abbasi A, Chen H, Salem A. Sentiment analysis in multiplelanguages: Feature selection for opinion classification in Webforums. Trans. Inf. Syst., 2008, 26(3): Article No. 12.

[5] Demartini G, Siersdorfer S. Dear search engine: What's youropinion about: Sentiment analysis for semantic enrichmentof web search results. In Proc. SEMSEARCH 2010, NewYork, USA, April 2010, Article No.4.

[6] Devitt A, Ahmad K. Sentiment polarity identification in fi-nancial news: A cohesion-based approach. In Proc. ACL2007, Prague, Czech Republic, June 2007, pp.984-991.

[7] O'Hare N, Davy M, Bermingham A, Ferguson P, Sheridan P,Gurrin C, Smeaton A F. Topic-dependent sentiment analysisof financial blogs. In Proc. CIKM Workshop on TSA 2009,New York, USA, Nov. 2009, pp.9-16.

[8] Ding X, Liu B, Yu P S. A holistic lexicon-based approachto opinion mining. In Proc. WSDM 2008, New York, USA,April 2008, pp.231-240.

[9] Morinaga S, Yamanishi K, Tateishi K, Fukushima T. Miningproduct reputations on the Web. In Proc. SIGKDD 2002,New York, USA, July 2002, pp.341-349.

[10] Riloff E, Wiebe J. Learning extraction patterns for subjectiveexpressions. In Proc. EMNLP 2003, Stroudsburg, PA, USA,July 2003, pp.105-112.

[11] Turney P D. Thumbs up or thumbs down?: Semantic orienta-tion applied to unsupervised classification of reviews. In Proc.ACL 2002, Stroudsburg, PA, USA, July 2002, pp.417-424.

[12] Pang B, Lee L. A sentimental education: Sentiment analysisusing subjectivity summarization based on minimum cuts. InProc. ACL 2004, Barcelona, Spain, July 2004, pp.271-278.

[13] Thet T T, Na J C, Khoo C S G. Aspect-based sentimentanalysis of movie reviews on discussion boards. Journal ofInformation Science, 2010, 36(6): 823-848.

[14] Wilson T, Wiebe J, Hoffmann P. Recognizing contextual po-larity in phrase-level sentiment analysis. In Proc. HLT-EMNLP 2005, Vancouver, British Columbia, Canada, Oct.2005, pp.347-354.

[15] Wilson T, Wiebe J, Hwa R. Recognizing strong and weakopinion clauses. Computational Intelligence, 2006, 22(2): 73-99.

[16] Nivre J. Dependency grammar and dependency parsing.Technical Report MSI report 05133, V?axjö University, Schoolof Mathematics and Systems Engineering, 2005.

[17] Jakob N, Weber S H, Muller M C, Gurevych I. Beyond thestars: Exploiting free-text user reviews to improve the accu-racy of movie recommendations. In Proc. CIKM Workshopon TSA 2009, Hong Kong, China, Nov. 2009, pp.57-64.

[18] Shaikh M A M, Prendinger H, Ishizuka M. Sentiment assess-ment of text by analyzing linguistic features and contextualvalence assignment. Appl. Artif. Intell., 2008, 22(6): 558-601.

[19] Liu B. Web Data Mining: Exploring Hyperlinks, Contentsand Usage Data (1st edition). Springer Berlin Heidelberg,New York, 2006, pp.37-54.

[20] Osman D J, Yearwood J, Vamplew P. Weblogs for market re-search: Finding more relevant opinion documents using sys-tem fusion. Online Information Review, 2009, 33(5): 873-888.

[21] Hu M, Liu B. Mining and summarizing customer reviews. InProc. the 10th SIGKDD, Seattle, WA, USA, Aug. 2004,pp.168-177.

[22] Kim S M, Hovy E. Determining the sentiment of opinions. InProc. the 20th COLING, Geneva, Switzerland, 2004, pp.1367-1373.

[23] Zhang C, Zeng D, Li J, Wang F Y, Zuo W. Sentiment analy-sis of Chinese Documents: From sentence to document level.Journal of the American Society for Information Science andTechnology, 2009, 60(12): 2474-2487.

[24] Na J C, Thet T T, Khoo C. Comparing sentiment expressionin movie reviews from four online genres. Online InformationReview, 2010, 34(2): 317-338.

[25] Moilanen K, Pulman S. Sentiment composition. In Proc.RANLP 2007, Borovets, Bulgaria, Sept. 2007, pp.378-382.

[26] Cohen J. A coefficient of agreement for nominal scales. Edu-cational and Psychological Measurement, 1960, 20(1): 37-46.

[27] Joshi M, Penstein-Rose C. Generalizing dependency featuresfor opinion mining. In Proc. ACL-IJCNLP 2009, Suntec,Singapore, Aug. 2009, pp.313-316.

[28] Agrawal R, Srikant R. Fast algorithms for mining associationrules in large databases. In Proc. VLDB 1994, Santiago deChile, Chile, Sept. 1994, pp.487-499.

[29] Wong K W, Zhou S, Yang Q, Yeung J M S. Mining customervalue: From association rules to direct marketing. Data Min-ing and Knowledge Discovery, 2005, 11(1): 57-79.

[30] Polanyi L, Zaenen A. Computing attitude and affect in text:Theory and applications. Computing Attitude and Affect inText: Theory and Applications, 2006, 20: 1-10.

[31] Quirk R, Greenbaum S, Leech G, Svartvik J. A Comprehen-sive Grammar of the English Language, Longman, 1985.

[32] Tan L K W, Na J C, Theng Y L, Chang K Y. Sentence-levelsentiment polarity classification using a linguistic approach.In Proc. ICADL 2011, Beijing, China, Oct. 2011, pp.77-87.
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[1] Feng Yulin;. Hierarchical Protocol Analysis by Temporal Logic[J]. , 1988, 3(1): 56 -69 .
[2] Shen Li;. Testability Analysis at Switch Level for CMOS Circuits[J]. , 1990, 5(2): 197 -202 .
[3] Zheng Chongxun; Zhang Kenong;. Orthogonal Algorithm of Logic Probability and Syndrome-Testable Analysis[J]. , 1990, 5(2): 203 -209 .
[4] Han Jianchao; Shi Zhongzhi;. Formalizing Default Reasoning[J]. , 1990, 5(4): 374 -378 .
[5] Huang Zhiyi; Hu Shouren;. Detection of And-Parallelism in Logic Programs[J]. , 1990, 5(4): 379 -387 .
[6] Hayong Zhou;. Analogical Learning and Automated Rule Constructions[J]. , 1991, 6(4): 316 -328 .
[7] Yao Xin; Li Guojie;. General Simulated Annealing[J]. , 1991, 6(4): 329 -338 .
[8] Li Weihua; Yuan Youguang;. Error Recovery in a Real-Time Multiprocessor System[J]. , 1992, 7(1): 83 -87 .
[9] Wu Xindong;. Inductive Learning[J]. , 1993, 8(2): 22 -36 .
[10] Tan Jianrong; Zheng Jianmin; Peng Qunsheng;. A Unified Algorithm for Finding the Intersection Curve of Surfaces[J]. , 1994, 9(2): 107 -116 .

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