Literature Review of Text and Multimodal Sentiment Analysis

Abstract

Sentiment analysis, also known as opinion mining, is a crucial branch of Natural language processing, which focuses on recognizing, extracting, and quantifying sentiment tendencies, emotional intensity and specific emotion types in textual data. With the rapid development of the internet and communication, analyzing sentiment contained in textual data becomes important and crucial for understanding public opinion, consumer behavior, and emotional trends. This paper provides a comprehensive review of sentiment analysis in the range of its application, evolution, task types, methodology and future development by analyzing the literature of this field. Sentiment analysis has developed from traditional lexicon-based methods to modern deep learning methods like CNN, RNN and transformer model, which have significantly improved accuracy and robustness. This paper also discussed challenges in sentiment analysis like sarcasm detection and cross-lingual analysis, and proposed potential solutions. The findings aim to provide comprehensive insight for researchers and contribute to innovations in sentiment analysis.