Sentiment Analysis of Consumer-Generated Online Reviews of Physical Bookstores Using Hybrid LSTM-CNN and LDA Topic Model

Abstract

Physical bookstore is the leader of cultural trend, the carrier of national reading and the provider of public cultural services, which embodies the cultural soft power of a city. The widely use of Internet e-commerce platform and the change of people's reading habits have brought great impact on physical bookstores, resulting in poor overall profitability of physical bookstores. In order to realize the sustainable development of physical bookstores, we mine and analyze consumer-generated online reviews. In this paper, a method of sentiment analysis based on Hybrid LSTM-CNN (Hybrid Long Short-Term Memory-Convolutional Neural Network) and LDA (Latent Dirichlet Allocation) topic model is proposed. Firstly, the Hybrid LSTM-CNN model is used to classify reviews, and then LDA topic model is used to extract features of positive and negative reviews. The results show that Hybrid LSTM-CNN model has better performance than the classic LSTM and CNN in sentiment classification. The LDA model mines that consumers have the positive attitude towards the products, context and ambiance of physical bookstores, and the negative attitude towards price and service. This method studies consumer-generated online reviews in physical bookstores from two aspects: sentiment classification and topic mining, which can help physical bookstore operators to know consumer feedback in time.