Analysis of neural networks in text sentiment

Abstract

The proliferation of digital media and online platforms has led to a significant increase in the production and distribution of textual data. Consequently, the study of text sentiment analysis has emerged as a crucial area of research within the domain of natural language processing. The objective of sentiment analysis is to detect and categorize emotional inclinations in text, which has substantial practical importance in comprehending public sentiment, monitoring brand reputation, and other related areas. This research does a comprehensive investigation of the utilization of neural network models in text sentiment analysis using literature review methods. It examines the difficulties encountered and the approaches employed to overcome these difficulties. The research encompasses the fundamental principles and techniques of text sentiment analysis utilizing neural networks, as well as the utilization of convolutional neural networks (CNN), recurrent neural networks (RNN)/long short-term memory networks (LSTM), attention mechanisms, and pre-trained models in the field of sentiment analysis. Furthermore, this study delves into the complexities associated with data noise, the interpretability of models, the analysis of long text and multimodal sentiment, as well as the utilization of transfer learning and domain adaptation techniques. The study's importance is in establishing a theoretical basis and practical recommendations for the advancement of sentiment analysis technology, particularly in enhancing model precision, comprehensibility, and range of applications, thereby delivering useful insights.