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Reseach Article

Different Applications and Techniques for Sentiment Analysis

by Suad Alhojely
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 5
Year of Publication: 2016
Authors: Suad Alhojely
10.5120/ijca2016912136

Suad Alhojely . Different Applications and Techniques for Sentiment Analysis. International Journal of Computer Applications. 154, 5 ( Nov 2016), 24-28. DOI=10.5120/ijca2016912136

@article{ 10.5120/ijca2016912136,
author = { Suad Alhojely },
title = { Different Applications and Techniques for Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 5 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number5/26488-2016912136/ },
doi = { 10.5120/ijca2016912136 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:25.870728+05:30
%A Suad Alhojely
%T Different Applications and Techniques for Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 5
%P 24-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment Analysis is a continuous field of research in the content mining field. Sentiment Analysis is the computational treatment of feelings, opinions, and subjectivity of content. This study paper handles an extensive review of the last upgrade in this field. Numerous as of late proposed calculations' improvements and different Sentiment Analysis applications are examined and displayed quickly in this review. Sentiment Analysis has as of late assumed a huge part for specialists since examination of online content is helpful for the statistical surveying political issue, business insight, on the web shopping, and logical overview from mental The related fields to Sentiment Analysis that pulled in analysts as of late are talked about. The fundamental focus of this review is to give almost a full picture of Sentiment Analysis is methods and the related fields with brief points of interest. The principle commitments of this paper incorporate the modern classifications of countless articles and the delineation of the late pattern of research in Sentiment Analysis and its related area.

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 Preprocessing 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 7140, 2012.
  18. Chetan Mate “Product Aspect Ranking using Sentiment Analysis: A Survey “Vol No 1,2015.
  19. Suad Alhojely. Sentiment Analysis and Opinion Mining: A Survey. Internationl Journal of Computer Application 150(6):22-25, September 2016.
  20. comScore/the Kelsey group. Online consumer- generated reviews have a significant impact on offline purchase behavior. Press Release, November 2007.
  21. Douglas W. Diamond. Reputation acquisition in debt markets. Journal of Political Economy, 97(4): 828–862, 1989.
  22. Benjamin Klein and Keith Leffler. The role of market forces in assuring contractual performance. Journal of Political Economy, 89(4):615–641, 1981.
  23. Carl Shapiro. Consumer information, product quality, and seller reputation. Bell Journal of Economics, 13(1):20–35, 1982.
  24. Carl Shapiro. Premiums for high quality products as returns to reputations. Quarterly Journal of Economics, 98(4):659–680, 1983.
  25. 10. APPENDEX
  26. Consumer Review Processing Aspect Identification Sentiment Classification
  27. Overall Rating Aspect Evaluation
  28. Figure: Architecture Diagram
  29. /
  30. Figure2: Sentiment Analysis Classification Technique
Index Terms

Computer Science
Information Sciences

Keywords

Opining Mining Sentiment Analysis Classification aspect ranking techniques