CFP last date
22 April 2024
Reseach Article

Analysis of Techniques of Sentiment and Topic Detection

by Supriya Paul, Sachin N. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 14
Year of Publication: 2015
Authors: Supriya Paul, Sachin N. Deshmukh
10.5120/20401-2709

Supriya Paul, Sachin N. Deshmukh . Analysis of Techniques of Sentiment and Topic Detection. International Journal of Computer Applications. 116, 14 ( April 2015), 1-4. DOI=10.5120/20401-2709

@article{ 10.5120/20401-2709,
author = { Supriya Paul, Sachin N. Deshmukh },
title = { Analysis of Techniques of Sentiment and Topic Detection },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 14 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number14/20401-2709/ },
doi = { 10.5120/20401-2709 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:05.059593+05:30
%A Supriya Paul
%A Sachin N. Deshmukh
%T Analysis of Techniques of Sentiment and Topic Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 14
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

User generated media like blogs, discussion forums is used as a tool by people to communicate their experiences with others. Presence of such huge data on Internet demands proper means to generate processed information from the unstructured data. What users need is more than mere sentiments. They need to know public opinion or experience of various aspects of a product like how is camera quality of the phone or energy efficiency of electronic products. For meeting the high demands of users, various techniques have been proposed till date. In this paper we are evaluating, all these techniques that discover topic along with sentiment associated with it. Many models were proposed to incorporate sentiment analysis with topic model to find aspects of a product and users sentiment about the aspect. Results of these models can be beneficial for various industries as well as users.

References
  1. Fangtao Li, Minlie Huang, Xiaoyan Zhu, J. 2010 Sentiment Analysis with Global Topics and Local Dependency. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10),1371-1376
  2. D. M. Blei, A. Y. Ng, and M. I. Jordan. J. 2007 Latent dirichlet allocation. J. Mach. Learn. Res. , 3:993–1022.
  3. C. Lin, and Y. He. 2009. Joint Sentiment/Topic Model for Sentiment Analysis, In 18th ACM Conference on Information and Knowledge Management (CIKM).
  4. Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai, J. 2007 Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs. Proc. 16th Int'l Conf. World Wide Web (WWW),pp. 171-180.
  5. I. Titov and R. McDonald 2008. A Joint Model of Text and Aspect Ratings for Sentiment Summarization, Proc. Assoc. Computational Linguistics—Human Language Technology (ACL-HLT), pp. 308-316.
  6. I. Titov and R. McDonald 2008. Modeling Online Reviews with MultiGrain Topic Models. Proc. 17th Int'l Conf. World Wide Web, pp. 111-120.
  7. T. Hofmann. 1999. Probabilistic Latent Semantic Indexing. Proc. 22nd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval. pp. 50-57.
  8. Yohan Jo, Alice ho. Feb 9–12 2011. Aspect and Sentiment Unification Model for Online Review Analysis. WSDM'11.
  9. KekeCai, Scott Spangler, Ying Chen, Li Zhang. Leveraging Sentiment Analysis for Topic Detection. International Conference on Web Intelligence and Intelligent Agent Technology(IEEE/WIC/ACM), pp. 265-271.
  10. Opinmind. http://www. opinmind. com.
  11. C. Lin, Yulan He, R. Everson. June 2012. Weakly Supervised Joint Sentiment-Topic Detection from Text. IEEE Transactions On Knowledge And Data Engineering, Vol. 24, No. 6 . pp. 1134-1145.
  12. DP. D. Turney. 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.
  13. Bing Liu. 2010. Sentiment Analysis and Subjectivity. Handbook of natural Language Processing, Second Edition.
  14. L. Zhuang, F. Jing, and X. -Y. Zhu, 2006, Movie review mining and summarization In Proceedings of the 15th ACM international conference on Information and knowledge management (CIKM), pp. 43–50.
Index Terms

Computer Science
Information Sciences

Keywords

Aspect detection sentiment analysis topic modeling opinion mining latent Dirichlet allocation (LDA).