Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey on Various Techniques for Sentiment Analysis and Opinion Mining

V. Maria Antoniate Martin, Dr. K. David, R. Bhuvaneswari


Sentiment analysis could be a range of language process for chase the mood of the general public a few unambiguous product or topic. Sentiment analysis, which is additionally called as opinion mining, involves in building a system to gather and examine opinions regarding the merchandise created in journal posts, comments, reviews or tweets. Here in this paper various sentiment analysis techniques are reviewed. The review sites mentioned for the awareness and various author’s proposals are tabulated.


Sentiment Analysis, Opinion Mining, Joint Segmentation, NLP, POS Tagging.

Full Text:



Minqing Hu , Bing Liu, Mining opinion features in customer reviews, Proceedings of the 19th national conference on Artifical intelligence, p.755-760, July 25-29, 2004, San Jose, California.

Brendan O’Connor, From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series Proceedings of the International AAAI Conference on Weblogs and Social Media, Washington, DC, May 2010.

Long Jiang, Target-dependent Twitter Sentiment Classification Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 151–160, Portland, Oregon, June 19-24, 2011.

Rui Xia, Dual Sentiment Analysis: Considering Two Sides of One Review IEEE Transactions on Knowledge and Data Engineering

B.Sudhakar and R. Bensraj, Interpreting the Public Sentiment variations on Twitter, Ieee Transactions On Knowledge And Data Engineering, Vol. 6, No. 1, September 2012

TheresaWilson, Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis, Proceedings of the 2003 conference on Empirical methods in natural language processing ,Pages 105-112

Jithe othersi Li and Eduard Hovy, Sentiment Analysis on the People’s Daily, J Li, EH Hovy - EMNLP, 2014

Ryan McDonald, Structured Models for Fine-to-Coarse Sentiment Analysis,

Ainur Yessenalina, Multi-level Structured Models for Document-level Sentiment Classification, National Science Foundation Grants BCS-0904822, BCS-0624277, IIS- 0535099; by a gift from Google; and by the Department of Homeland Security under ONR Grant N0014-07-1-0152.

Richard Socher, Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank.

Xavier Glorot, Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach, Dept. IRO, Universite de Montr_eal. Montr_eal (QC), H3C 3J7, Canada

Duyu Tang, Building Large-Scale Twitter-Specific Sentiment Lexicon: A Representation Learning Approach, Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 172–182, Dublin, Ireland, August 23-29 2014.

Furu Wei, Learning Sentiment-SpecificWord Embedding for Twitter Sentiment Classification, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1555–1565, Baltimore, Maryland, USA, June 23-25 2014.

Yongfeng Zhang, Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification, National High Technology Research and Development (863) Program (2011AA01A205) of China, and the third author is sponsored by the National Science Foundation (IIS-0713111), Volume 1, Number 1.

Alec Go, Twitter Sentiment Classification using Distant Supervision,

Hyun Duk Kim and ChengXiang Zhai, Generating Comparative Summaries of Contradictory Opinions in Text, CIKM’09, November 2–6, 2009, Hong Kong, China.

SidaWang and Christopher D. Manning, Baselines and Bigrams: Simple, Good Sentiment and Topic Classification, benchmarking against BNB is untrustworthy, cf. (McCallum and Nigam, 1998).

Xiaowen Ding, A Holistic Lexicon-Based Approach to Opinion Mining, WSDM’08, February 11-12, 2008, Palo Alto, California, USA.ung-Chen Chou, Chih-Hung Lin, Pao-Ching Li, Yu-Chiang Li, “A (2, 3) Threshold Secret Sharing Scheme Using Sudoku”, Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IEEE 2010.

Preslav Nakov, SemEval-2013 Task 2: Sentiment Analysis in Twitter,D11-1141, Named Entity Recognition in Tweets: An Experimental Study(self citation),2011.

AndrewL.Maas, LearningWord Vectors for Sentiment Analysis,

Maite Taboada, Lexicon-BasedMethods for Sentiment Analysis, Submission received: 14 December 2009; revised submission received: 22 August 2010; accepted for publication: 28 September 2010. Volume 37, Number 2

Bo Pang, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, Pages 79-86.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.