Abstract
Sentiment Classification is a continuing area of research in text mining. Sentiment Analysis the automatic representation of the ideas, emotions and subjectivity of text, whose purpose is to define the polarity of the content of text, and opinion of the expresses in the form of binary ratings such as likes or dislikes, or a more granular set of choices, such as a 1 to 5 rating. This paper focuses primarily on high-level, end-to-end workflow to solve text classification problems using machine learning algorithm such as Naive-Bayes classifier for text classification issues to mining opinions and Amazon User Reviews.
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References
Yu L-C, Wu J-L, Chang P-C, Chu H-S (2013) Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowl Based Syst 41:89–97
Xu T, Peng Q, Cheng Y (2012) Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowl Based Syst 35:279–289
Maks I, Vossen P (2012) A lexicon model for deep sentiment analysis and opinion mining applications. Dec Support Syst 53:680–688
Djaballah KA, Boukhalfa K, Boussaid O (2019) Sentiment analysis of Twitter messages using Word2vec by weighted average. In: Sixth international conference on social networks analysis, management and security (SNAMS)
Guner L, Coyne E, Smit J (March, 2019) Sentiment analysis for Amazon.com reviews. https://www.researchgate.net/publication/332622380
Haque TU, Saber NN, Shah FM (2018) Sentiment analysis on large scale amazon product reviews. In: 2018 IEEE international conference on innovative research and development (ICIRD)
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Srikanth, K., Murthy, N.V.E.S., Prasad Reddy, P.V.G.D. (2021). Sentiment Classification on Online Retailer Reviews. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_140
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DOI: https://doi.org/10.1007/978-981-15-7961-5_140
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