Text Sentiment Analysis Method Based on Support Vector Machine And Long Short-term Memory Network

Abstract

Machine learning is a hot technology today and plays a pivotal role in text sentiment analysis [1]. Text has complex properties such as semantic word order grammar and contextual relationship, so the accuracy of text sentiment analysis faces significant challenges. There are some classic methods in the industry for text sentiment analysis, such as Support Vector Machines (SVM) and Naive Bayes[2]. These methods are strongly related to feature extraction, with high complexity and average performance. With the development of neural network technology, people began to use neural network models for text sentiment analysis, but compared with traditional methods, neural network processing corpus is more accurate, but slower. Therefore, this paper adopts the method of combining classical algorithm model and neural network model for text sentiment analysis, which can improve the processing efficiency without changing the accuracy.