Opinion mining of customer feedback data on the web

As people leave on the Web their opinions on products and services they have used, it has become important to develop methods of (semi-)automatically classifying and gauging them. The task of analyzing such data, collectively called customer feedback data, is known as opinion mining. Opinion mining consists of several steps, and multiple techniques have been proposed for each step. In this paper, we survey and analyze various techniques that have been developed for the key tasks of opinion mining. On the basis of our survey and analysis of the techniques, we provide an overall picture of what is involved in developing a software system for opinion mining.

[1]  Wayne Niblack,et al.  Sentiment mining in WebFountain , 2005, 21st International Conference on Data Engineering (ICDE'05).

[2]  Shlomo Argamon,et al.  Automatically Determining Attitude Type and Force for Sentiment Analysis , 2007, LTC.

[3]  Oscar Firschein,et al.  Readings in computer vision: issues, problems, principles, and paradigms , 1987 .

[4]  Andrea Esuli,et al.  Determining Term Subjectivity and Term Orientation for Opinion Mining , 2006, EACL.

[5]  Hiroshi Kanayama,et al.  Deeper Sentiment Analysis Using Machine Translation Technology , 2004, COLING.

[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]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[9]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss classification , 2005, CIKM '05.

[10]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[11]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.

[12]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[13]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[14]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Bing Liu,et al.  Mining Opinion Features in Customer Reviews , 2004, AAAI.

[16]  Eric Chang,et al.  Red Opal: product-feature scoring from reviews , 2007, EC '07.

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

[18]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[19]  Jeannett Martin,et al.  The Language of Evaluation: Appraisal in English , 2005 .

[20]  Shlomo Argamon,et al.  Using appraisal groups for sentiment analysis , 2005, CIKM '05.

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

[22]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

[23]  Kathleen R. McKeown,et al.  Predicting the semantic orientation of adjectives , 1997 .

[24]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.