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Reseach Article

Improving the Performance in Sentiment Analysis

by Sumanto Kar, J. Scriptu Rajan, Sebastian Dmello, Sapna Prabhu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 7
Year of Publication: 2021
Authors: Sumanto Kar, J. Scriptu Rajan, Sebastian Dmello, Sapna Prabhu
10.5120/ijca2021921359

Sumanto Kar, J. Scriptu Rajan, Sebastian Dmello, Sapna Prabhu . Improving the Performance in Sentiment Analysis. International Journal of Computer Applications. 183, 7 ( Jun 2021), 19-24. DOI=10.5120/ijca2021921359

@article{ 10.5120/ijca2021921359,
author = { Sumanto Kar, J. Scriptu Rajan, Sebastian Dmello, Sapna Prabhu },
title = { Improving the Performance in Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 7 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number7/31940-2021921359/ },
doi = { 10.5120/ijca2021921359 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:08.457769+05:30
%A Sumanto Kar
%A J. Scriptu Rajan
%A Sebastian Dmello
%A Sapna Prabhu
%T Improving the Performance in Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 7
%P 19-24
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment Analysis is the contextual mining of text which determines whether the given piece of words is positive, negative, or neutral. The main objective of this work is to make a system that rates movies based on the user's comments made on the movie. The system analyses the data in order to check for user sentiments associated with each comment and gathers all the comments made on a particular movie. It then calculates an average rating in order to score it. The system model checks for sentimental keywords and predicts user sentiment associated with it. Also, the system works on the sarcastic comments in order to find whether the comment is positive or not. Various Python libraries and Django Web Server has been used for the pre-processing of data.

References
  1. Sagar et al Sentiment Analysis of Movie Ratings System. International conference on computing and virtualization (ICCCV-17)
  2. A.Jeyapriya et al Extracting aspects and mining opinions in product reviews using supervised learning algorithms. In the proceedings of 2nd International Conference on Electronics and Communication Systems (ICECS), 2015
  3. Mostafa Karamibekret et al Sentiment Analysis of Social Issues. In the Proceedings of International Conference on Social Informatics (Social Informatics), 2012
  4. B. Liu. Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2010.
  5. T. Nasukawa and J. Yi. Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture, pages 70–77. ACM, 2003.
  6. S. Somasundaran and J. Wiebe. Recognizing stances in ideological online debates. In Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 116–124. ACM, 2010.
  7. Wikipedia: The free encyclopedia, 2004. https://en.wikipedia.org/wiki/Sentiment_analysis
  8. Alessia D’Andrea and Fernando Ferri. Approaches, Tools and Applications for Sentiment Analysis Implementation. In proceedings of International Journal of Computer Applications, 2009
  9. M. Balamurugan and S. Kannan, Analyse the performance of Ensemble Classifiers using Sampling Techniques. In proceedings of ICTACT Journal on Soft Computing, July 2016
  10. Paperspace Blog, 2021. https://blog.paperspace.com/bagging-ensemble-methods/
  11. S. R. Das and M. Y. Chen. Yahoo! for amazon: Sentiment extraction from small talk on the web. Management Science, 53(9):1375–1388, 2007.
  12. C. Fellbaum. Wordnet: An electronic lexical database.
  13. V. P. H. Binali and W. Chen. A state-of-the art opinion mining and its application domains. In IEEE International Conference on Industrial Technology, pages 1–6, February 2009.
  14. F. Inc. The free dictionary, 2012.
  15. W. F. Inc. Wikipedia: The free encyclopedia, 2004. https://en.wikipedia.org/wiki/Text_mining
  16. C. M. Kristina Toutanova, Dan Klein and Y. Singer. Feature-rich part of-speech tagging with a cyclic dependency network. In HLT-NAACL, pages 252–259. ACM, 2003.
  17. S. L. Kushal Dave and D. M. Pennock. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web, pages 519–528. ACM, 2003.
  18. B. Liu. Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2010.
  19. S. C.-O. Michael Gamon, Anthony Aue and E. Ringger. Pulse: Mining customer opinions from free text. In Proceedings of the 6th International Symposium on Intelligent Data Analysis, pages 121–132, 2005.
  20. T. Nasukawa and J. Yi. Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture, pages 70–77. ACM, 2003.
  21. Anju Joshi et al Aspect Level Opinion Mining on Customer Reviews using Support Vector Machine. International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 6, Issue 7, July 2017
  22. The App Solutions https://theappsolutions.com/blog/development/sentiment-analysis/
  23. Hyejung Chung and Kyung-shik Shin Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction. MDPI Journal on Sustainability
  24. B. Pang and L. Lee. Opinion Mining and Sentiment Analysis. The Essense of Knowledge, 2008.
  25. B. Pang, L. Lee, and S. Vaithyanathan. Sentiment classification using machine learning techniques. In EMNLP02 Proceedings, pages 79–86. Association for Computational Linguistics, 2002.
  26. S. Shandilya and S. Jain. Automatic opinion extraction from web documents. In Proceeding of International Conference on Computer and Automation Engineering, pages 351–355, March 2009.
  27. S. Somasundaran and J. Wiebe. Recognizing stances in ideological online debates. In Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 116–124. ACM, 2010.
  28. P. D. Turney. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting, pages 417–424. Association for Computational Linguistics (ACL), 2002.
  29. B. L. Xiaowen Ding and P. S. Yu. A holistic lexicon-based approach to opinion mining. In Proceedings of the international conference on Web Search and web Data Mining, pages 231–240. ACM, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning LSTM Model Movie Review System Sentiment Analysis Sarcasm Classifier Bagging Algorithm.