Abstract
Sentiment analysis is a novel approach towards analyzing the sentiment of a given text or document. In particular, the sentiment analysis of short texts such as single sentences and social media tweets and comments are one of the main challenges of natural language processing (NLP) given the limited contextual information they usually convey. In recent years, deep learning application has shown promising result in natural language processing. This paper presents the comparative analysis of various deep learning, traditional machine learning and lexicon based methodologies of sentiment analysis. We present the importance of pre-processing and feature engineering for sentiment analysis on a resource constrained data-set of Manipuri comments gathered from social media platform using these approaches.
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References
Feldman et al (2013) Techniques and applications for sentiment analysis. Commun ACM
Bautin M, Vijayrenu L, Skiena S (2008) International sentiment analysis for news and blogs. In: Second international conference on weblogs and social media ICWSM
Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the association for computational linguistics, pp 417–424
Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. In: Proceedings of the first international conference on information technology and quantitative management (ITQM 2013). Suzhou, China
Zhao J, Gui X (2017) Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access 5:2870–2879
Giulio A, Ferrari L, Fontanini T, Fornacciari P, Iotti E, Magliani F, Manicardi S (2016) A comparison between pre-processing techniques for sentiment analysis in Twitter. In: Proceedings of the 2nd international workshop on knowledge discovery on the WEB, Cagliari, Italy, 8–10 Sept 2016
Prasad S (2010) Micro-blogging sentiment analysis using Bayesian classification methods
Vapnik V (1995) The nature of statistical learning theory
Durant K, Smith M (2006) Mining sentiment classification from political web logs. In: Proceedings of workshop on web mining and web usage analysis of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. Philadelphia
Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis : a survey. Data mining and knowledge discovery. Wiley Interdisciplinary Reviews. https://doi.org/10.1002/widm.1253
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Asghar, Dr. Muhammad, Kundi F, Khan A, Ahmad S (2014) Lexicon-based sentiment analysis in the social web. J Basic Appl Sci Res 4:238–248
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Singh, T.D., Singh, T.J., Shadang, M., Thokchom, S. (2021). Review Comments of Manipuri Online Video: Good, Bad or Ugly. In: Maji, A.K., Saha, G., Das, S., Basu, S., Tavares, J.M.R.S. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 170. Springer, Singapore. https://doi.org/10.1007/978-981-33-4084-8_5
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DOI: https://doi.org/10.1007/978-981-33-4084-8_5
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