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Identifying Suggestions for Improvement of Product Features from Online Product Reviews

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9471)

Abstract

Online forums are used to share experiences and opinions about products and services. These forums range from review sites such as Amazon (www.amazon.com) to online social networks such as Twitter (www.twitter.com). The user-generated content in these platforms capture the users’ opinions and sentiments. In this work, we explore the problem of identifying suggestions from text content. The paper first defines suggestive intent and then presents a supervised learning approach to identify text that contains suggestive intent. The results show high accuracy with a F1 score of 0.93.

Keywords

  • Computational Linguistics
  • Sentiment Score
  • Supervise Learning Approach
  • North American Chapter
  • Sentiment Polarity

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Harsh Jhamtani .

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Jhamtani, H., Chhaya, N., Karwa, S., Varshney, D., Kedia, D., Gupta, V. (2015). Identifying Suggestions for Improvement of Product Features from Online Product Reviews. In: Liu, TY., Scollon, C., Zhu, W. (eds) Social Informatics. SocInfo 2015. Lecture Notes in Computer Science(), vol 9471. Springer, Cham. https://doi.org/10.1007/978-3-319-27433-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-27433-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27432-4

  • Online ISBN: 978-3-319-27433-1

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