Methods for Sentiment Analysis: A Literature Study

EOI: 10.11242/viva-tech.01.01.08

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Shiv Dhar, Suyog Pednekar, Kishan Borad, Ashwini Save, "Methods for Sentiment Analysis: A Literature Study", VIVA-Tech IJRI Volume 1, Issue 1, Article 8, pp. 1-8, Oct 2018. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic study of these opinions can lead to information which can prove to be valuable for many companies and industries in future. A huge number of users are online, and they share their opinions and comments regularly, this information can be mined and used efficiently. Various companies can review their own product using sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient processing to collect this data and analyze it to produce required result. In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network, morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows various accuracy results obtained by all the systems.


Machine Learning, Sentiment Analysis, CNN, analysis, AI, SVM, NLP.


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  2. Z. Nasim, Q. Rajput and S. Haider, "Sentiment analysis of student feedback using machine learning and lexicon based approaches," 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), Langkawi, 2017, pp. 1-6."
  3. Z. Xiangyu, L. Hong and W. Lihong, "A context-based regularization method for short-text sentiment analysis," 2017 International Conference on Service Systems and Service Management, Dalian, 2017, pp. 1-6.
  4. M. H. Krishna, K. Rahamathulla and A. Akbar, "A feature based approach for sentiment analysis using SVM and coreference resolution," 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2017, pp. 397-399.
  5. P. Yadav and D. Pandya, "SentiReview: Sentiment analysis based on text and emoticons," 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, 2017, pp. 467-472.
  6. Y. Gao, W. Rong, Y. Shen and Z. Xiong, "Convolutional Neural Network based sentiment analysis using Adaboost combination," 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 1333-1338.
  7. B. Duncan and Y. Zhang, "Neural networks for sentiment analysis on Twitter," 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Beijing, 2015, pp. 275-278.
  8. M. Trupthi, S. Pabboju and G. Narasimha, "Sentiment Analysis on Twitter Using Streaming API," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 915-919.
  9. K. Liu, Y. Niu, J. Yang, J. Wang and D. Zhang, "Product Related Information Sentiment-Content Analysis Based on Convolutional Neural Networks for the Chinese Micro-Blog," 2016 International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, 2016, pp. 357-361.
  10. D. V. N. Devi, C. K. Kumar and S. Prasad, "A Feature Based Approach for Sentiment Analysis by Using Support Vector Machine," 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, 2016, pp. 3-8.
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  12. R. Hegde and Seema S., "Aspect based feature extraction and sentiment classification of review data sets using Incremental machine learning algorithm," 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 122-125.
  13. B. Wang and L. Min, “Deep Learning for Aspect-Based Sentiment Analysis.” (2015)
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