Opinion Mining and Analysis: A survey

The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available in digital form. One important problem in sentiment analysis of product reviews is to produce summary of opinions based on product features. We have surveyed and analyzed in this paper, various techniques that have been developed for the key tasks of opinion mining. We have provided an overall picture of what is involved in developing a software system for opinion mining on the basis of our survey and analysis.

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

[2]  Hua Xu,et al.  Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints , 2010, COLING.

[3]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[4]  James R. Curran,et al.  Adding Noun Phrase Structure to the Penn Treebank , 2007, ACL.

[5]  Nidhi Mishra,et al.  Classification of Opinion Mining Techniques , 2012 .

[6]  Nilesh M. Shelke,et al.  Survey of Techniques for Opinion Mining , 2012 .

[7]  Khairullah Khan,et al.  Identifying Product Features from Customer Reviews using Lexical Concordance , 2012 .

[8]  Debanjan Mahata,et al.  A clustering and opinion mining approach to socio-political analysis of the blogosphere , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[9]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

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

[11]  Ivan Koychev,et al.  Computationally effective algorithm for information extraction and online review mining , 2012, WIMS '12.

[12]  Dietrich Klakow,et al.  Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives , 2009, NODALIDA.

[13]  Manabu Torii,et al.  A Hybrid Approach to Sentiment Sentence Classification in Suicide Notes , 2012, Biomedical informatics insights.

[14]  Charles Elkan,et al.  Expectation Maximization Algorithm , 2010, Encyclopedia of Machine Learning.

[15]  Dekang Lin,et al.  Automatic Retrieval and Clustering of Similar Words , 1998, ACL.

[16]  Hua Xu,et al.  Clustering product features for opinion mining , 2011, WSDM '11.

[17]  Wei Peng,et al.  Generate Adjective Sentiment Dictionary for Social Media Sentiment Analysis Using Constrained Nonnegative Matrix Factorization , 2021, ICWSM.

[18]  C. Fellbaum An Electronic Lexical Database , 1998 .

[19]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[20]  James R. Curran,et al.  Parsing Noun Phrases in the Penn Treebank , 2011, Computational Linguistics.

[21]  Akshi Kumar,et al.  Sentiment Analysis: A Perspective on its Past, Present and Future , 2012 .

[22]  Bakhtawar Seerat,et al.  Opinion Mining: Issues and Challenges (A survey) , 2012 .

[23]  Xuanjing Huang,et al.  Phrase Dependency Parsing for Opinion Mining , 2009, EMNLP.

[24]  Claire Cardie,et al.  Multi-Level Structured Models for Document-Level Sentiment Classification , 2010, EMNLP.

[25]  Vandana Jagtap,et al.  Analysis of different approaches to Sentence-Level Sentiment Classification , 2013 .

[26]  Pattarachai Lalitrojwong,et al.  Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization , 2010, J. Univers. Comput. Sci..

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