Abstract
The most popular microblogging social media is Twitter, where people express their point of view toward different things. They bought different products and give their unbiased opinion on Twitter. In this paper, a novel method is proposed to get the opinions of customers toward the product or company and analyze it. The product-related Tweets are gathered by Twitter with the help of the browser. This method is not using Twitter API. The proposed methods are defined for labeling Tweets in three emotions, that is, positive, negative, and neutral efficiently. Sentiment analysis models are used for classifying the opinions of the customer toward that product and toward individual features of the product. Using these results, it improves the future versions of the product. This research technique helps and improves the marketing approaches and high selling of product as well. It predicts the best and worst feature of the product with respect to customer opinion with an accuracy of 93.62%.
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Sengar, C.P.S., Nirmala, S.J. (2020). Label Assignment and Sentimental Analysis for a Product Review on Twitter Data. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_18
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DOI: https://doi.org/10.1007/978-981-15-2475-2_18
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