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Response Scrunity Class on Online Shopping inspection

  • Unique Paper ID: 148057
  • Volume: 5
  • Issue: 12
  • PageNo: 210-215
  • Abstract:
  • To exploit the sentiment of texts for learning more powerful continuous word representations. It proposes to capture both context and sentiment level evidences. The nearest neighbor in the embedding space are not only semantically similar but also favor to have the same sentiment polarity, so that it is able to separate positive and negative of both the spectrum. It enables Content based filtering algorithm which encodes the sentiment of texts in continuous word representation. Customer opinions plays the major role in the E-commerce applications such as Flipkart, eBay etc. Users find it as a difficult process by potential buyers to choose a product is worth or not via online. In the proposed system, the various emotion analysis techniques is used to provide a solution in two main aspects. Extract customer opinions on specific product or seller. Analyze the sentiments towards that specific product or seller. In this paper, we analyzed various belief mining techniques and sentiment analysis and then the performance be improved which produces best accuracy.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 5
  • Issue: 12
  • PageNo: 210-215

Response Scrunity Class on Online Shopping inspection

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