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Knowledge Generation Using Sentiment Classification Involving Machine Learning on E-Commerce
Abstract
Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naïve Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.
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