ABSTRACT
One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly
- A. Andreevskaia and S. Bergler. Mining WordNet for Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses. In EACL'06, pp. 209--216, 2006.Google Scholar
- P. Beineke, T. Hastie, C. Manning, and S. Vaithyanathan. An Exploration of Sentiment Summarization. In Proc. of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, 2003.Google Scholar
- G. Carenini, R. Ng, and A. Pauls. Interactive Multimedia Summaries of Evaluative Text. IUI'06, 2006. Google ScholarDigital Library
- S. Das, and M. Chen. Yahoo! for Amazon: Extracting market sentiment from stock message boards. APFA'01, 2001.Google Scholar
- K. Dave, S. Lawrence, and D. Pennock. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. WWW'03, 2003. Google ScholarDigital Library
- X. Ding and B. Liu. The Utility of Linguistic Rules in Opinion Mining." SIGIR-2007 (poster paper). Google ScholarDigital Library
- A. Esuli and F. Sebastiani, EACL-06, 2006. Determining Term Subjectivity and Term Orientation for Opinion Mining, EACL-06, 2006.Google Scholar
- C. Fellbaum. WordNet: an Electronic Lexical Database, MIT Press, 1998.Google ScholarCross Ref
- M. Gamon, A. Aue, S. Corston-Oliver, and E. K. Ringger. Pulse: Mining customer opinions from free text. IDA'2005. Google ScholarDigital Library
- V. Hatzivassiloglou and J. Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. COLING'00, 2000. Google ScholarDigital Library
- V. Hatzivassiloglou and K. McKeown. Predicting the Semantic Orientation of Adjectives. ACL-EACL'97, 1997. Google ScholarDigital Library
- M. Hearst. Direction-based Text Interpretation as an Information Access Refinement. In P. Jacobs, editor, Text-Based Intelligent Systems. Lawrence Erlbaum Associates, 1992. Google ScholarDigital Library
- M. Hu and B. Liu. Mining and summarizing customer reviews. KDD'04, 2004. Google ScholarDigital Library
- N. Jindal, and B. Liu. Mining Comparative Sentences and Relations. In AAAI'06, 2006. Google ScholarDigital Library
- N. Kaji and M. Kitsuregawa. Automatic Construction of Polarity-Tagged Corpus from HTML Documents. COLING/ACL'06, 2006. Google ScholarDigital Library
- H. Kanayama and T. Nasukawa. Fully Automatic Lexicon Expansion for Domain-Oriented Sentiment Analysis. EMNLP'06, 2006. Google ScholarDigital Library
- S. Kim and E. Hovy. Determining the Sentiment of Opinions. COLING'04, 2004. Google ScholarDigital Library
- S. Kim and E. Hovy. Automatic Identification of Pro and Con Reasons in Online Reviews. COLING/ACL 2006. Google ScholarDigital Library
- N. Kobayashi, R. Iida, K. Inui and Y. Matsumoto. Opinion Mining on the Web by Extracting Subject-Attribute-Value Relations. In Proc. of AAAI-CAAW'06, 2006.Google Scholar
- L.-W. Ku, Y.-T. Liang and H.-H. Chen. Opinion Extraction, Summarization and Tracking in News and Blog Corpora. In Proc. of the AAAI-CAAW'06, 2006.Google Scholar
- B. Liu, M. Hu, M. and J. Cheng. Opinion Observer: Analyzing and comparing opinions on the Web. WWW-05, 2005. Google ScholarDigital Library
- S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima, Mining Product Reputations on the Web. KDD'02, 2002. Google ScholarDigital Library
- T. Nasukawa and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. K-CA-2003. Google ScholarDigital Library
- V. Ng, S. Dasgupta and S. M. Niaz Arifin. Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews. ACL'06, 2006. Google ScholarDigital Library
- NLProcessor ¿ Text Analysis Toolkit. 2000. http://www.infogistics.com/textanalysis.html.Google Scholar
- B. Pang and L. Lee, Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales. ACL'05, 2005. Google ScholarDigital Library
- B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment Classification Using Machine Learning Techniques. EMNLP'2002, 2002. Google ScholarDigital Library
- A-M. Popescu and O. Etzioni. Extracting Product Features and Opinions from Reviews. EMNLP-05, 2005. Google ScholarDigital Library
- E. Riloff and J. Wiebe. 2003. Learning extraction patterns for subjective expressions. EMNLP'2003, 2003. Google ScholarDigital Library
- V. Stoyanov and C. Cardie. Toward opinion summarization: Linking the sources. In Proc. of the Workshop on Sentiment and Subjectivity in Text, 2006. Google ScholarDigital Library
- R. Tong. An Operational System for Detecting and Tracking Opinions in on-line discussion. SIGIR 2001 Workshop on Operational Text Classification, 2001.Google Scholar
- P. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. ACL'02, 2002. Google ScholarDigital Library
- T. Wilson, J. Wiebe, and R. Hwa. Just how mad are you? Finding strong and weak opinion clauses. AAAI'04, 2004. Google ScholarDigital Library
- J. Wiebe, and R. Mihalcea. Word Sense and Subjectivity. In ACL'06, 2006. Google ScholarDigital Library
- J. Wiebe, and E. Riloff: Creating Subjective and Objective sentence classifiers from unannotated texts. CICLing, 2005. Google ScholarDigital Library
- H. Yu, V. Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. EMNLP'2003. Google ScholarDigital Library
- L. Zhuang, F. Jing, X.-Yan Zhu, and L. Zhang. Movie Review Mining and Summarization. CIKM-06, 2006. Google ScholarDigital Library
Index Terms
- A holistic lexicon-based approach to opinion mining
Recommendations
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementAspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are ...
Mining slang and urban opinion words and phrases from cQA services: an optimization approach
WSDM '12: Proceedings of the fifth ACM international conference on Web search and data miningCurrent opinion lexicons contain most of the common opinion words, but they miss slang and so-called urban opinion words and phrases (e.g. delish, cozy, yummy, nerdy, and yuck). These subjectivity clues are frequently used in community questions and are ...
A Structure for Opinion in Social Domains
SOCIALCOM '13: Proceedings of the 2013 International Conference on Social ComputingOpinion mining, as a sub-field of text mining, analyzes opinions expressed regarding an object, a topic, or an issue. An opinion is expressed by a person using some opinion terms or phrases regarding a target. Statistical studies show that the affective ...
Comments