Bi-LSTM based sentiment analysis on the reviews of several mobile phone brands

Abstract

Nowadays, the development of computing power has made the users of different kinds of social, electric business platforms transfer from information receivers to publishers. And this situation stimulates the need for sentiment analysis. Generally speaking, there are two main methods for sentiment analysis, namely the sentiment lexicon methods and machine learning methods. The current research focuses on sentiment analysis with deep learning method. In order to explore the applicability of deep learning technology in analyzing consumers' psychology in the real world as a tool of sentiment analysis, this paper first uses a crawler to obtain the reviews from the consumers on two major e-commerce platforms in China, focusing on the latest models of four mobile phone brands, such as Apple, Huawei, Xiaomi and Samsung. Then, 20,000 reviews are collected and sentiment marked. The preprocessed data is combined with keyword extraction method to set the first and second level classification, and the data under each classification is input into the text sentiment analysis model based on Bi-LSTM and sentiment lexicon. By scoring the reviews, the sentiment orientation of users to different mobile phone brands can be obtained. Finally, this study also gives corresponding suggestions and countermeasures. This set of analysis models can help mobile phone developers dynamically monitor the sentiment changes of consumers and grasp the trend of the mobile phone market environment in time.