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.
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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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