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Reseach Article

A Critical Review of Sentiment Analysis

by Fatehjeet Kaur Chopra, Rekha Bhatia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 10
Year of Publication: 2016
Authors: Fatehjeet Kaur Chopra, Rekha Bhatia
10.5120/ijca2016911592

Fatehjeet Kaur Chopra, Rekha Bhatia . A Critical Review of Sentiment Analysis. International Journal of Computer Applications. 149, 10 ( Sep 2016), 37-40. DOI=10.5120/ijca2016911592

@article{ 10.5120/ijca2016911592,
author = { Fatehjeet Kaur Chopra, Rekha Bhatia },
title = { A Critical Review of Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 10 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number10/26037-2016911592/ },
doi = { 10.5120/ijca2016911592 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:54:26.292091+05:30
%A Fatehjeet Kaur Chopra
%A Rekha Bhatia
%T A Critical Review of Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 10
%P 37-40
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis is the process which helps in recognizing people’s perspective and emotional conditions. Sentiment analysis appeared in the roots of the disciplines of psychology, sociology and anthropology. Sentiment analysis takes place from the hypothesis of emotive attitude and assessment hypothesis. It emphasis on sensation in forming perceptions. Feelings that are generated from both conscious and unconscious processing are called emotions. The feelings of the people can be expressed in positive or negative ways. Mostly, parts of speech are used as feature to extract the sentiment of the text. Sentiment analysis is an evolving field with a variety of use applications. Further, the evaluation of the accuracy of the existing systems, from which it is analyzed that the result can be improved by calculating the sentiments of word instead of calculating sentiment of complete sentence or paragraph.

References
  1. Kaur, A., & Gupta, V. (Nov. 2013). A survey on sentiment analysis and opinion mining techniques. Proceedings of Journal of Emerging Technologies in Web Intelligence , Vol.5, No. 4. 62.
  2. Jeonghee Yi, T. N. (2003). “Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques”. Proceedings of the Third IEEE International Conference on Data Mining . IEEE.
  3. Pang B, L. L. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval , 1-2 (2), 1-135.
  4. Wilson T, W. J. (2005). Recognizing contextual polarity. In: Proceedings of HLT/EMNLP.
  5. Yu Liang-Chih, W. J.-L.-C. (2013). Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock. Knowl-Based Syst , (pp. 41:89–97).
  6. Michael Hagenau, M. L. (2013). Automated news reading: stock price prediction based on financial news using context-capturing features. Decis Supp Syst.
  7. Tao Xu, P. Q. (2012). Identifying the semantic orientation of terms using S-HAL for sentiment analysis. (pp. 35:279–89). Knowl-Based Syst .
  8. Maks Isa, V. P. (2012). A lexicon model for deep sentiment analysis and opinion mining applications. (pp. 53:680–8). Decis Support Syst.
  9. Tsytsarau Mikalai, P. T. (2012). Survey on mining subjective data on the web. (pp. 24:478–514). Data Min Knowl Discov. 64.
  10. B, L. (2012). Sentiment analysis and opinion mining. Synth Lect.
  11. Cambria E, S. B. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intell Syst (pp. 28:15–21). IEEE.
  12. Feldman, R. (2013). Techniques and applications for sentiment analysis. Commun ACM , (pp. 56:82–9).
  13. Montoyo Andre´ s, M.-B. P. (2012;). Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. (pp. 53:675–9). Decis Support Syst.
  14. Yao, R., & Chen, J. (2013). Predicting Movie Sales Revenue using Online Reviews. International Conference on Granular Computing (GrC). IEEE.
  15. Singh, V., & Piryani, R. (2013). Sentiment Analysis of Movie Reviews A new feature –based heuristic for aspect-level sentiment classification. IEEE.
  16. K. Dave, S. L. (2003). “Mining the Peanut Gallery-Opinion Extraction and Semantic Classification of Product Reviews”. Proceedings of the 12th International World Wide Web Conference, (pp. 519-528).
  17. Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proc. 40th Annual Meeting on Association for Computational Linguistics (ACL), Philadelphia , US.
  18. F.Sebastiani, A. E. (2005). “Determining the Semantic Orientation of terms through gloss analysis”. Proceedings of CIKM-05, 14th ACM International Conference on Information and Knowledge Management (pp. 617-624,). Bremen, DE, CIKM.
  19. Varghese, R. (2004). Opinion Mining Based on Feature-Level Aspect Based Sentiment Analysis using Support Vector Machine Classifier. International Conference on Advances in Computing, Communications and Informatics (ICACCI),ICACCI.Heba Khudhair Abass(2013), “A Study of Digital Image Fusion Techniques Based on Contrast and Correlation Measures”.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Opinion mining NLP Linguistic Resources Web data Analysis Information Retrieval