Survey of sentiment analysis and its impact on data extraction
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Published
Sep 8, 2021
Narahari Ajmeera
Dr. P. Kamakshi
Dr. B. Vishnu Vardhan
Abstract
With the advent of information exploration over the years, the most important thing to make the business decision is to consider the opinion of the peoples. Sometimes the opinions which are transformed to sentiments plays a vital role in the aforementioned areas. The opinion of the people is shared by the e-commerce, social media networks like twitter, facebook, blogs, forums, and etc., The opinion is categorized into positive, negative and neutral opinion. To extract the sentiments, opinions there are different approaches available in the literature such as support vector machine, Naïve Bayes, Neural Networks, N-gram, lexicon based approaches [12-15] etc. In this paper it is aimed at comparing different type of machine learning approaches such as supervised [7], unsupervised and semi supervised learning algorithms which are useful to extract the various opinions over the Net. It is further surveyed to understand various performance measures when the extracted opinions were transformed to sentiments.
How to Cite
Ajmeera, N., P, K., & B, V. V. (2021). Survey of sentiment analysis and its impact on data extraction. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/199
Article Details
Keywords
Machine Learning, NLP tools, Natural Language Processing (NLP), Sentiment Analysis
References
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[2] B. Amina and T. Azim, "SCANCPECLENS: A Framework for Automatic Lexicon Generation and Sentiment Analysis of Micro Blogging Data on China Pakistan Economic Corridor," in IEEE Access, vol. 7, pp. 133876-133887, 2019, doi: 10.1109/ACCESS.2019.2940528.
[3] Esha, Sharma A.K. (2019) An Intelligent Framework for Sentiment Analysis of Text and Emotions Implementation of Restaurants’ Case Study. In: Rathore V., Worring M., Mishra D., Joshi A., Maheshwari S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_86.
[4] Jagdale R.S., Shirsat V.S., Deshmukh S.N. (2019) Sentiment Analysis on Product Reviews Using Machine Learning Techniques. In: Mallick P., Balas V., Bhoi A., Zobaa A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_61.
[5] M. H. Abd El-Jawad, R. Hodhod and Y. M. K. Omar, "Sentiment Analysis of Social Media Networks Using Machine Learning," 2018 14th International Computer Engineering Conference (ICENCO), Cairo, Egypt, 2018, pp. 174-176, doi: 10.1109/ICENCO.2018.8636124.
[6] S. Aloufi and A. E. Saddik, "Sentiment Identification in Football-Specific Tweets," in IEEE Access, vol. 6, pp. 78609-78621, 2018, doi: 10.1109/ACCESS.2018.2885117.
[7] B. Samal, A. K. Behera and M. Panda, "Performance analysis of supervised machine learning techniques for sentiment analysis," 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS), Chennai, India, 2017, pp. 128-133, doi: 10.1109/SSPS.2017.8071579.
[8] U. Kumari, A. K. Sharma and D. Soni, "Sentiment analysis of smart phone product review using SVM classification technique," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2017, pp. 1469-1474, doi: 10.1109/ICECDS.2017.8389689.
[9] K. Schouten, O. van der Weijde, F. Frasincar and R. Dekker, "Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data," in IEEE Transactions on Cybernetics, vol. 48, no. 4, pp. 1263-1275, April 2018, doi: 10.1109/TCYB.2017.2688801.
[10] E. Tyagi and A. K. Sharma, "An intelligent framework for sentiment analysis of text and emotions - A review," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017, pp. 3297-3302, doi: 10.1109/ICECDS.2017.8390069.
[11] Singh, J., Singh, G. & Singh, R. Optimization of sentiment analysis using machine learning classifiers. Hum. Cent. Comput. Inf. Sci. 7, 32 (2017). https://doi.org/10.1186/s13673-017-0116-3.
[12] F. Luo, C. Li and Z. Cao. (2016). "Affective-feature-based sentiment analysis using SVM classifier," 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanchang, China, pp. 276-281, doi: 10.1109/CSCWD.2016.7566001.
[13] Khan, W., Ghazanfar, M.A., Azam, M.A. et al. (2020). Stock market prediction using machine learning classifiers and social media, news. J Ambient Intell Human Comput https://doi.org/10.1007/s12652-020-01839-w.
[14] Chiranjeevi P., Teja Santosh D., Vishnuvardhan B. (2019) Survey on Sentiment Analysis Methods for Reputation Evaluation. In: Mallick P., Balas V., Bhoi A., Zobaa A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_6.
[15] Vishal.A.Kharde, Sheetal.Sonawane, (2016). “Sentiment analysis of Twitter data: A survey of Techniques”, arXiv preprint arXiv: 1601.06971, volume 139.
[16] Tumasjan, A., et al. (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. ICWSM.
Section
GE3- Computers & Information Technology