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.
Cite
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9471, pp. 112–119). Springer Verlag. https://doi.org/10.1007/978-3-319-27433-1_8
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
