A Combined Approach for Effective Features Extraction from Online Product Reviews

Abstract

Today E-commerce websites provide customers with the needed product information by giving various kinds of services to choose from. One such service is to allow the customer to read the end user online reviews. Online reviews contain features which are useful for the analysis in opinion mining. Converting these unstructured reviews into useful information require extracting the product features from them. Natural Language Processing (NLP) based technique extracts various kinds of product features including the low frequency features. Topic Modeling based approach also identifies specific product features from the online reviews. The effective number of product features is made available to the customer when these two approaches are combined. This results in the expanded product feature set so that the customer makes wise decisions without having to compromise on the feature set.

Construction of Jiangxi Province’s “Dual Cycle” Logistics Economic Evaluation System Based on Entropy Weight Grey Correlation Method

“…The logistics industry is a basic, strategic, and leading industry that effectively connects production and consumption and supports the development of the national economy [1]. It is one of the four leading industries that will focus on the development of the national modern service industry during the 14th Five-Year Plan.…”