A Survey On Opinion Mining and Sentiment Analysis Using R - Programming

Authors(1) :-Sravani S

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. Sentiment analysis (also known as opinion mining) refers to the use of natural language processing (NLP), text analysis and computational linguistics to identify and extract subjective information from the source materials .The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. Generally speaking, sentiment analysis aims to determine the attitude of a writer or a speaker with respect to a specific topic or the overall contextual polarity of a document. Globally, business enterprises can leverage opinion polarity and sentiment topic recognition to gain deeper understanding of the drivers and the overall scope. Subsequently, these insights can advance competitive intelligence and improve customer service, thereby creating a better brand image and providing a competitive edge.

Authors and Affiliations

Sravani S
Assistant Professor, Department of Computer Applications, K.B.N College, P.G Centre, Vijayawada, Andhra Pradesh, India

Natural Language Processing, Opinion Mining

  1. R K. Ando and T. Zhang, "A framework for learning predictive struc- tures from multiple tasks and unlabeled data," Journal of Machine Learning Research, vol. 6, pp. 1817-1853, 2005.
  2. A Andreevskaia and S. Bergler, "Mining WordNet for a fuzzy sentiment: Sen- timent tag extraction from WordNet glosses," in Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), 2006.
  3. W Antweiler and M. Z. Frank, "Is all that talk just noise? The informa- tion content of internet stock message boards," Journal of Finance, vol. 59, pp. 1259-1294, 2004.
  4. N Archak, A. Ghose, and P. Ipeirotis, "Show me the money! Deriving the pricing power of product features by mining consumer reviews," in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2007.
  5. S Argamon, ed., Proceedings of the IJCAI Workshop on DOING IT WITH STYLE: Computational Approaches to Style Analysis and Synthesis. 2003.
  6. S Argamon, J. Karlgren, and J. G. Shanahan, eds., Proceedings of the SIGIR Workshop on Stylistic Analysis of Text For Information Access. ACM, 2005.
  7. S Argamon, J. Karlgren, and O. Uzuner, eds., Proceedings of the SIGIR Work- shop on Stylistics for Text Retrieval in Practice. ACM, 2006.
  8. Tsur, D. Davidov, and A. Rappoport, " A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews". In Proceeding of ICWSM.of Context Dependent Opinions,2010.
  9. Weitong Huang, Yu Zhao, Shiqiang Yang, Yuchang Lu, "Analysis of the user behavior and opinion classification based on the BBS" , Applied Mathematics and Computation 205 (2008) 668-676 .
  10. Wiebe, W.-H. Lin, T. Wilson and A. Hauptmann, "Which side are you on? Identifying perspectives at the document and sentence levels," in Proceedings of the Conference on Natural Language Learning (CoNLL), 2006.
  11. Yi and Niblack, "Sentiment Mining in Web Fountain",Proceedings of 21st international Conference on Data Engineering, pp. 1073-1083, Washington DC,2005.
  12. Yongyong Zhail, Yanxiang Chenl, Xuegang Hu, "Extracting Opinion Features in Sentiment Patterns" ,International Conference on Information, Networking and Automation (ICINA),2010.
  13. YuanbinWu, Qi Zhang, Xuanjing Huang, LideWu, "Phrase Dependency Parsing for Sentiment analysis", Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 1533-1541, Singapore, 6-7 August 2009

Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 375-383
Manuscript Number : CSEIT183777
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sravani S, "A Survey On Opinion Mining and Sentiment Analysis Using R - Programming", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.375-383, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT183777

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