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2020 | OriginalPaper | Chapter

Enhancing Personalized Response to Product Queries Using Product Reviews Incorporating Semantic Information

Authors : Payal Aich, Manju Venugopalan, Deepa Gupta

Published in: Advances in Data and Information Sciences

Publisher: Springer Singapore

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Abstract

E-commerce is very much in trend in the current era for selling/purchasing products online. Hence, consumers tend to visit question answer forums to know about a product before making a purchase. The proposed work is to build a web application which would form an alternative to a traditional question answer system. Rather than focusing on a knowledge-based question answer system, the proposed work attempts to mine reviews related to the product and provides critical reviews on the product which are relevant to the question asked by the consumer. This application would be used by any user looking for supporting critical reviews related to product functionalities. Given a question answer dataset and a review dataset on a product, the similarity between the questions and the reviews is calculated and three top reviews which are most relevant to the question along with their relevance score are the output of the system. The model uses powerful similarity measures based on WordNet and Word embedding in addition to the basic similarity measures based on cosine similarity and TF-IDF. The model is evaluated in terms of how well the sentiment extracted from the output reviews of the proposed model confines with that of the answer in the question answer dataset.

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Footnotes
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Metadata
Title
Enhancing Personalized Response to Product Queries Using Product Reviews Incorporating Semantic Information
Authors
Payal Aich
Manju Venugopalan
Deepa Gupta
Copyright Year
2020
Publisher
Springer Singapore
Book
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_47