Online Reviews Sentiment Analysis and Product Feature Improvement with Deep Learning
Jihua Cao, Jie Li, Miao Yin, Yunfeng Wang
Abstract
The text mining of online reviews is currently a popular research direction of e-commerce and is considered the next blue ocean. Online reviews can dig out consumer preferences and provide theoretical guidance for the improvement of product features. However, current research mostly focuses on sentiment analysis methods and rarely involves feature extraction and large-scale data recognition. This article uses word segmentation technology to create a new feature extraction method. With the long short-term memory neural network and latent Dirichlet allocation topic model, we propose a product feature improvement model—CESC (Consumer online reviews–Extract short text–Sentiment analysis–Cluster feature). The model can derive the product features and attitudes that consumers prefer based on consumer online reviews and use it to improve product features. According to the experimental results of three electronic products sold on the e-commerce platform, the model can effectively dig out consumer preferences for online reviews. Enterprises can improve the quality of products and services, better meet the needs of consumers, promote consumers’ consumption, and achieve the enterprises’ goals and values.