A SURVEY ON SENTIMENT ANALYSIS AND OPINION MINING

Sentiment analysis is a machine learning approach i n which machines analyze and classify the human’s s entiments, emotions, opinions etc about some topic which are expressed i n the form of either text or speech. The textual da ta available in the web is increasing day by day. In order to enhance the sale s of a product and to improve the customer satisfac tion, most of the on-line shopping sites provide the opportunity to customers to write reviews about products. These reviews are l rge in number and to mine the overall sentiment or opinion polarity from all of them, sentiment analysis can be used. Manua l an lysis of such large number of reviews is practically impossible. Theref o automated approach of a machine has significant role in solving this hard problem. The major challenge of the area of Sentime nt analysis and Opinion mining lies in identifying the emotions expressed in these texts. This literature survey is done to stud y the sentiment analysis problem in-depth and to fa mili rize with other works done on the subject.

[1]  Xiangji Huang,et al.  Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain , 2012, IEEE Transactions on Knowledge and Data Engineering.

[2]  Vibhu O. Mittal,et al.  Comparative Experiments on Sentiment Classification for Online Product Reviews , 2006, AAAI.

[3]  Jun Guo,et al.  Exploiting Combined Multi-level Model for Document Sentiment Analysis , 2010, 2010 20th International Conference on Pattern Recognition.

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

[5]  Ming Xu,et al.  Feature-level sentiment analysis for Chinese product reviews , 2011, 2011 3rd International Conference on Computer Research and Development.

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

[7]  Danushka Bollegala,et al.  Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus , 2013, IEEE Transactions on Knowledge and Data Engineering.

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

[9]  Ellen Riloff,et al.  Creating Subjective and Objective Sentence Classifiers from Unannotated Texts , 2005, CICLing.

[10]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

[11]  Pushpak Bhattacharyya,et al.  Feature Specific Sentiment Analysis for Product Reviews , 2012, CICLing.

[12]  Razvan C. Bunescu,et al.  Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques , 2003, Third IEEE International Conference on Data Mining.

[13]  Sharon Goldwater,et al.  Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing , 2005 .

[14]  Donald A. Adjeroh,et al.  Proximity-based sentiment analysis , 2011, Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2011).

[15]  Ali Selamat,et al.  Sentiment analysis using Support Vector Machine , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[16]  Khairullah Khan,et al.  Sentence based sentiment classification from online customer reviews , 2010, FIT.

[17]  Chih-Ping Wei,et al.  Classifying web review opinions for consumer product analysis , 2009, ICEC.

[18]  J. K. Sing,et al.  Development of a novel algorithm for sentiment analysis based on adverb-adjective-noun combinations , 2012, 2012 3rd National Conference on Emerging Trends and Applications in Computer Science.

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

[20]  Fei Liu,et al.  A clustering-based approach on sentiment analysis , 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.