CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Literature Review on Feature Identification in Sentiment Analysis

by Altaf Hussain, Shafaq Sattar, Muhammad Tanvir Afzal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 3
Year of Publication: 2015
Authors: Altaf Hussain, Shafaq Sattar, Muhammad Tanvir Afzal
10.5120/ijca2015907331

Altaf Hussain, Shafaq Sattar, Muhammad Tanvir Afzal . Literature Review on Feature Identification in Sentiment Analysis. International Journal of Computer Applications. 132, 3 ( December 2015), 22-27. DOI=10.5120/ijca2015907331

@article{ 10.5120/ijca2015907331,
author = { Altaf Hussain, Shafaq Sattar, Muhammad Tanvir Afzal },
title = { Literature Review on Feature Identification in Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 3 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number3/23575-2015907331/ },
doi = { 10.5120/ijca2015907331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:09.812858+05:30
%A Altaf Hussain
%A Shafaq Sattar
%A Muhammad Tanvir Afzal
%T Literature Review on Feature Identification in Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 3
%P 22-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now-a-days the volume of opinions about products, issues, events, and politics etc. on different social, e-commerce and review sites grows very rapidly. From both opinion holder and opinion target point of view, it is very difficult and time consuming task to analyze all the reviews from this massive amount of data on the web. So, there is a need of efficient method that automatically extracts the opinions and relevant features of the opinion target from the reviews and finally generates the feature wise summary. Sometimes people may use different words to express same feature, this may produce a misperception in the results during feature wise summary generation. To avoid this, we need to categorize similar features for precise classification of opinions based on these feature groups. Therefore, our study is targeting the most important tasks of feature based sentiment analysis that are feature extraction and feature categorization. This paper is about to cover the currently available techniques in these two areas. We have also focused on least addressed area in this domain giving an opportunity to researchers for future work.

References
  1. Liu, B. 2012. Sentiment Analysis and Opinion Mining (Synthesis Lectures on Human Language Technologies). Morgan & Claypool Publishers.
  2. Rashid, A., Anwer, N., Iqbal, M., and Sher, M. 2013. A survey paper: areas, techniques and challenges of opinion mining. IJCSI International Journal of Computer Science Issues, 10(2), 18-31.
  3. Khushboo, T. N., Vekariya, S. K., & Mishra, S. 2012. Mining of Sentence Level Opinion Using Supervised Term Weighted Approach of Naïve Bayesian Algorithm. International Journal of Computer Technology and Applications, 3(3).
  4. Motwani, M., and Tiwari, A. 2014. Comparison of Novel Semi supervised Text classification using BPNN by Active search with KNN Algorithm. International Journal on Computer Science and Engineering, 6(5), 181.
  5. Qiu, G., Liu, B., Bu, J., and Chen, C. 2009. Expanding Domain Sentiment Lexicon through Double Propagation. In IJCAI (Vol. 9, pp. 1199-1204).
  6. Zhang, L., Liu, B., Lim, S. H., and O'Brien-Strain, E. 2010. Extracting and ranking product features in opinion documents. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (pp. 1462-1470). Association for Computational Linguistics.
  7. Htay, S. S., and Lynn, K. T. 2013. Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews. The Scientific World Journal, 2013.
  8. Varghese, R., and Jayasree, M. 2013. Aspect based Sentiment Analysis using support vector machine classifier. In Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on(pp. 1581-1586). IEEE.
  9. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  10. Kang, D., and Park, Y. 2014. Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041-1050.
  11. Bolanle Adefowoke Ojokoh, Olufunke Catherine Olayemi, and Olumide Sunday Adewale. 2013. Generating Recommendation Status of Electronic Products from Online Reviews. Intelligent Control & Automation (2153-0653) 4.1
  12. Wogenstein, F., Drescher, J., Reinel, D., Rill, S., and Scheidt, J. 2013. Evaluation of an algorithm for aspect-based opinion mining using a lexicon-based approach. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 5). ACM.
  13. Dalal, M. K., & Zaveri, M. A. 2013. Semisupervised learning based opinion summarization and classification for online product reviews. Applied Computational Intelligence and Soft Computing, 2013, 10.
  14. Anwer, N., Rashid, A., & Hassan, S. 2010. Feature based opinion mining of online free format customer reviews using frequency distribution and Bayesian statistics. In Networked Computing and Advanced Information Management (NCM), 2010 Sixth International Conference on (pp. 57-62). IEEE.
  15. Patel, Falguni N., and Neha R. Soni. 2012. Text mining: A Brief survey. International Journal of Advanced Computer Research 2.4.
  16. Dong, R., Schaal, M., O'Mahony, M. P., and Smyth, B. 2013. Topic extraction from online reviews for classification and recommendation. InProceedings of the Twenty-Third international joint conference on Artificial Intelligence (pp. 1310-1316). AAAI Press.
  17. Hu, M., and Liu, B. 2004. Mining opinion features in customer reviews. InAAAI (Vol. 4, No. 4, pp. 755-760).
  18. Siqueira, H., and Barros, F. 2010. A feature extraction process for sentiment analysis of opinions on services. In Proceedings of International Workshop on Web and Text Intelligence.
  19. Lee, H., Chang, A., Peirsman, Y., Chambers, N., Surdeanu, M., and Jurafsky, D. 2013. Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics, 39(4), 885-916.
  20. Lee, H., Peirsman, Y., Chang, A., Chambers, N., Surdeanu, M., and Jurafsky, D. 2011. Stanford's multi-pass sieve coreference resolution system at the CoNLL-2011 shared task. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task (pp. 28-34). Association for Computational Linguistics.
  21. Sleator, D. D., and Temperley, D. 1995. Parsing English with a link grammar.arXiv preprint cmp-lg/9508004.
  22. Church, K. W., and Hanks, P. 1990. Word association norms, mutual information, and lexicography. Computational linguistics, 16(1), 22-29.
  23. Zhang, W., Yoshida, T., and Tang, X. 2008. Tfidf, lsi and multi-word in information retrieval and text categorization. In Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on (pp. 108-113). IEEE.
  24. Zhang, W., Yoshida, T., and Tang, X. 2007. Text classification using multi-word features. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on (pp. 3519-3524). IEEE.
  25. Mei, J., Lan, Y. and Gao, Y. 1983. Synonyms lexicon. 1st Edition. Shanghai Dictionary Publishing House, Shanghai.
  26. Ma, B., Zhang, D., Yan, Z., and Kim, T. 2013. An Lda and Synonym Lexicon Based Approach to Product Feature Extraction from Online Consumer Product Reviews. Journal of Electronic Commerce Research, 14(4), 304-314.
  27. Zhai, Z., Liu, B., Xu, H., and Jia, P. 2010. Grouping product features using semi-supervised learning with soft-constraints. In Proceedings of the 23rd International Conference on Computational Linguistics (pp. 1272-1280). Association for Computational Linguistics.
  28. Zhai, Z., Liu, B., Xu, H., and Jia, P. 2011. Constrained LDA for grouping product features in opinion mining. In Advances in knowledge discovery and data mining (pp. 448-459). Springer Berlin Heidelberg.
  29. Guo, H., Zhu, H., Guo, Z., Zhang, X., and Su, Z. 2009. Product feature categorization with multilevel latent semantic association. InProceedings of the 18th ACM conference on Information and knowledge management (pp. 1087-1096). ACM.
  30. Liu, L., Lv, Z., and Wang, H. 2013. Extract product features in Chinese web for opinion mining. Journal of Software, 8(3), 627-632.
  31. Zhai, Z., Liu, B., Xu, H., and Jia, P. 2011. Clustering product features for opinion mining. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 347-354). ACM.
  32. Syed, A. Z., Aslam, M., and Martinez-Enriquez, A. M. 2014. Associating targets with SentiUnits: a step forward in sentiment analysis of Urdu text. Artificial Intelligence Review, 41(4), 535-561.
  33. Jo, Y., and Oh, A. H. 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 815-824). ACM.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Grouping Feature Extraction Feature Identification Feature Clustering Sentiment Analysis