Opinion observer: analyzing and comparing opinions on the Web

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.

[1]  Bing Liu,et al.  Web data extraction based on partial tree alignment , 2005, WWW '05.

[2]  Ellen Riloff,et al.  Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.

[3]  Kamal Nigam,et al.  Towards a Robust Metric of Opinion , 2004 .

[4]  Marti A. Hearst.  Direction-based text interpretation as an information access refinement , 1992 .

[5]  Ramakrishnan Srikant,et al.  Mining newsgroups using networks arising from social behavior , 2003, WWW '03.

[6]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[7]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[8]  Barbara Rosario,et al.  Classifying Semantic Relations in Bioscience Texts , 2004, ACL.

[9]  Doug Downey,et al.  Web-scale information extraction in knowitall: (preliminary results) , 2004, WWW '04.

[10]  Béatrice Daille,et al.  Study and Implementation of Combined Techniques for Automatic Extraction of Terminology , 1994 .

[11]  Ed H. Chi,et al.  eBooks with indexes that reorganize conceptually , 2004, CHI EA '04.

[12]  Craig A. Knoblock,et al.  A hierarchical approach to wrapper induction , 1999, AGENTS '99.

[13]  Christian Jacquemin,et al.  Term Extraction and Automatic Indexing , 2005 .

[14]  Janyce Wiebe,et al.  Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.

[15]  Razvan C. Bunescu,et al.  Collective Information Extraction with Relational Markov Networks , 2004, ACL.

[16]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[17]  Juliana Freire,et al.  WebViews: accessing personalized web content and services , 2001, WWW '01.

[18]  Andrew McCallum,et al.  Information Extraction with HMM Structures Learned by Stochastic Optimization , 2000, AAAI/IAAI.

[19]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[20]  Slava M. Katz,et al.  Technical terminology: some linguistic properties and an algorithm for identification in text , 1995, Natural Language Engineering.

[21]  Janyce Wiebe,et al.  Development and Use of a Gold-Standard Data Set for Subjectivity Classifications , 1999, ACL.

[22]  Satoshi Morinaga,et al.  Mining product reputations on the Web , 2002, KDD.

[23]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[24]  Allison Woodruff,et al.  "User Interfaces" and the Social Negotiation of Availability , 2004 .

[25]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[26]  Jim Holder,et al.  User interfaces , 1985, ALET.

[27]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

[28]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[29]  Peter D. Turney.  Thumbs Up, Thumbs Down , 2013, Journal of Cell Science.

[30]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

[31]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[32]  Robert L. Grossman,et al.  Mining data records in Web pages , 2003, KDD '03.

[33]  Jeonghee Yi,et al.  Sentiment analysis: capturing favorability using natural language processing , 2003, K-CAP '03.