Sentiment analysis based on long short-term memory model

Abstract

An important area of natural language processing is text emotion analysis. Emotion analysis is a very meaningful research work and has a broad application prospect, such as social media monitoring, reputation research of commodity brands, market research, and so on. By analyzing the time and content factors of the data, considering the final application scenario, and finally comparing the advantages and disadvantages of the methods, There are three main techniques for analyzing emotions in text: emotion analysis using an emotion dictionary, emotion analysis using machine learning, and emotion analysis using deep learning. Among them, Convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory networks (LSTM) are examples of deep learning-based techniques. For processing temporal relate issues such as video, speech, and text, CNN algorithms often consume a large amount of computational time, especially for processing image datasets, which may encounter specific problems. To address this issue, RNN is more suitable for solving temporal related issues such as video, voice, and text. In natural language, word order is an extremely important feature. RNN may potentially process sequences of any length, add memory units based on the original neural network, handle pre- and post-word dependencies, and process sequences of any length. The main work is as follows: LSTM adds or deletes unit states through a structure called gate, Determine the experimental thinking of text analysis, Crawl and train data sets through AI Studio, pycharm and other tools.