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Reseach Article

Sentiment Analysis and Opinion Mining: A Survey

by Suad Alhojely
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 6
Year of Publication: 2016
Authors: Suad Alhojely
10.5120/ijca2016911545

Suad Alhojely . Sentiment Analysis and Opinion Mining: A Survey. International Journal of Computer Applications. 150, 6 ( Sep 2016), 22-25. DOI=10.5120/ijca2016911545

@article{ 10.5120/ijca2016911545,
author = { Suad Alhojely },
title = { Sentiment Analysis and Opinion Mining: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 6 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 22-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number6/26097-2016911545/ },
doi = { 10.5120/ijca2016911545 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:12.570850+05:30
%A Suad Alhojely
%T Sentiment Analysis and Opinion Mining: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 6
%P 22-25
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this age, in this nation, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions (Abraham Lincoln, 1858 ) [1]. It is apparent from President Lincoln's well known quote that legislators understood the force of open assumption quite a while prior. In today world, the Internet is the main source of information. An enormous amount of information and opinion online is scattered and unstructured with no machine to arrange it. Because of demand the public to know opinions about exact product and services, political issues, or social scientists. That’s led us to study of field Opining Mining and Sentiment Analysis. Opining Mining and Sentiment Analysis have recently played a significant role for researchers because analysis of online text is beneficial for the market research political issue, business intelligence, online shopping, and scientific survey from psychological. Sentiment Analysis identifies the polarity of extracted public opinions. This paper presents a survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification.

References
  1. Goutam Chakraborty, Murali Pagolu, Satish Garla, “Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS” Pp 181-184, 2013.
  2. T. Nasukawa, “Sentiment Analysis: Capturing Favorability Using Natural Language Processing Definition of Sentiment Expressions,” pp. 70–77, 2003.
  3. K. Dave, I. Way, S. Lawrence, and D. M. Pennock, “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews,” 
2003.
  4. E. Marrese-Taylor, J. D. Velasquez, and F. Bravo-Marquez, “Opinion Zoom: A Modular Tool to Explore Tourism Opinions on the Web,” 2013 IEEE/WIC/ACM Int. Jt. Conf. Web Intell. Intell. Agent Technol., pp. 261–264, Nov. 2013.
  5. E. Haddi, X. Liu, and Y. Shi, “The Role of Text Pre-processing in Sentiment Analysis,” Procedia Comput. Sci., vol. 17, pp. 26–32, Jan. 2013.
  6. R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Syst. Appl., vol. 40, no. 2, pp. 621–633, Feb. 2013.
  7. R. Arora and S. Srinivasa, “A Faceted Characterization of the Opinion Mining Landscape,” pp. 1–6, 2014.
  8. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Found. Trends® Inf. Retr., vol. 2, no. 1–2, pp. 1–135, 2008.
  9. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up? sentiment classification using machine learning techniques," presented at the Proceedings of the ACL-02 conferenceon Empirical methods in natural language processing - Volume 10, 2002.
  10. D. Bespalov, B. Bai, A. Shokoufandeh, and Y. Qi, “Sentiment Classi fi cation Based on Supervised Latent n-gram Analysis,” pp. 375–382, 2011.
  11. M. Gamon, “Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis.”
  12. H. Yu and V. Hatzivassiloglou. “Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the 2003 conference on Empirical methods in natural language processing, EMNLP '03, pages 129-136.
  13. Zheng-Jun Zha, Jianxing Yu, Jinhui Tang, Meng Wang, Tat-Seng Chua,” Product Aspect Ranking and Its Applications”. IEEE2014.
  14. Bing Liu, “Sentiment Analysis and Opinion Mining” pp.7-140,2012.
  15. Anand Mahendran and Anjali Duraiswany, “Opinion Mining for text classification”, International Journal of Scientific Engineering and Technology (2277-1581), Vol No.2,2013
  16. M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. SIGKDD, Seattle, WA, USA, 2004, pp. 168–177.
  17. Bing, Liu “sentiment analysis and opining mining” pp 7-140, 2012.
  18. Chetan Mate “Product Aspect Ranking using Sentiment Analysis: A Survey “ Vol No 1,2015.
Index Terms

Computer Science
Information Sciences

Keywords

Opining Mining Sentiment Analysis Classification aspect ranking techniques