Text sentiment analysis based on the LSTM method

Abstract

Sentiment analysis is an important research direction in the field of natural language processing. With the rise of various social network platforms, a large number of network users have begun to express and publish their opinions on X, LINE, Weibo, and other platforms, which makes sentiment analysis more and more important. However, as more and more people use social platforms, the number of comments from Internet users has surged, and Internet buzzwords and Internet popular topics are constantly changing. This phenomenon leads to an increase in the error rate of traditional text emotion detection methods, which in turn leads to a reduction in the operating efficiency of some network platforms. In addition, nowadays, sentiment analysis has a wider and more urgent need in many fields, especially in the business field. In order to cope with this change and demand, this paper focuses on a text sentiment analysis model based on a neural network model.