Abstract
The sentiments of the users are expressed in the form of views or comments, in favor or against of any item, a product or a movie, etc. These reviews may be labeled or unlabeled. Labeled reviews are easier to process in compare to that of unlabeled once. Using Semi supervised machine-learning technique; the unlabeled reviews can be labeled. In this approach, with the help of small amount of labeled reviews, a large volume of unlabeled review can be labeled. In this paper, a step-by-step approach is adopted to label the unlabeled dataset. In order to perform this task, Support Vector Machine (SVM) technique is used. In order to access the results in each steps, the performance of the used technique is evaluated using different parameters like precision, recall and accuracy and thus, overall process can move forward.
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References
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Feldman, R.: Techniques and applications for sentiment analysis. Communications of the ACM 56(4), 82–89 (2013)
B. Pang, L. Lee, A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts, in: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, pp. 271 – 276, 2004
G. Gautam, D. Yadav, Sentiment analysis of twitter data using machine learning approaches and semantic analysis, in: Contemporary Computing (IC3), 2014 Seventh International Conference on, IEEE, pp. 437–442, 2014
Hastie, T., Tibshirani, R., Friedman, J.: Unsupervised learning. Springer (2009)
M. F. A. Hady, F. Schwenker, Semi-supervised learning, in: Handbook on Neural Information Processing, Springer, pp. 215–239, 2013
A. B. Goldberg and X. Zhu, Seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization, in: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 45–52, 2006
V. Sindhwani and P. Melville, Document-word co-regularization for semi-supervised sentiment analysis, In: Proceeding of Eighth IEEE International Conference on Data Mining, pp. 1025–1030, IEEE press, Italy, 2008,
Anand, D., Naorem, D.: Semi-supervised aspect based sentiment analysis for movies using review filtering. Procedia Computer Science 84, 86–93 (2016)
Silva, N.F.F.D., Coletta, L.F., Hruschka, E.R.: A survey and comparative study of tweet sentiment analysis via semi-supervised learning. ACM Computing Surveys (CSUR) 49(1), 15–25 (2016)
Khan, F.H., Qamar, U., Bashir, S.: Swims: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis. Knowledge-Based Systems 100, 97–111 (2016)
Zhai. X, Oliver. A., Kolesnikov. A., Beyer. L., S4l: Self-supervised semi-supervised learning, In: Proceedings of the IEEE international conference on computer vision, pp. 1476-1485, 2019
H. Zhao, J. Zheng, W. Deng, Y. Song,: Semi-Supervised Broad Learning System Based on Manifold Regularization and Broad Network, in: IEEE Transactions on Circuits and Systems, 67(3), pp. 983-994, 2020
Van Engelen. J.E. and Hoos. H.H.,: A survey on semi-supervised learning, In: Machine Learning, 109(2), pp. 373-440, 2020
Tripathy, A., Agrawal, A., Rath, S.K.: Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications 57, 117–126 (2016)
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, :Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1., pp. 142–150 Association for Computational Linguistics, 2011
Tripathy, A., Anand, A., Rath, S.K.: Document-level sentiment classification using hybrid machine learning approach. Knowledge and Information Systems 53(3), 805–831 (2017)
R. Garreta and G. Moncecchi, Learning scikit-learn: machine learning in python, Packt Publishing Ltd, 2013
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Tripathy, A., Jena, A.K. (2021). Sentiment Analysis Using Semi Supervised Machine Learning Technique. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_28
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DOI: https://doi.org/10.1007/978-981-33-6081-5_28
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