Abstract
Product reviews from customers are plays vital role for the customers who want to buy the product through online. To get the knowledge of customer emotions on particular item and its features given by the owner of the product required efficient sentiments investigation. Investigating emotions of customers from huge and complex unstructured reviews is big challenge. There are lot of text mining approaches have been proposed by many researchers for understanding the different characteristics of the customer connectedness on items based on reviews. Still need a better approach for investigation of emotions which can help to improve the business. Major risks in text mining are, find out the spelling problems, links, special symbols and irrelevant phases. In this paper main objective is to framing the relationship between different emotions. For this purpose machine learning techniques are applied and evaluated
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Kumar, P.K., Nandagopalan, S., Swamy, L.N. (2019). Investigation of Emotions on Purchased Item Reviews Using Machine Learning Techniques. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-00979-3_43
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