Review of Methods and Applications of Text Sentiment Analysis

被引：0
作者：
Jiawa Z. [1 ,2 ]
Wei L. [1 ]
Sili W. [1 ]
Heng Y. [1 ]
机构：
[1] Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou
[2] School of Economics and Management, University of Chinese Academy of Sciences, Beijing
来源：
Data Analysis and Knowledge Discovery | 2021年 / 54卷 / 06期
关键词：
Deep Learning; Machine Learning; Sentiment Analysis; Sentiment Lexicon;
D O I：
10.11925/infotech.2096-3467.2021.0040
中图分类号：
学科分类号：
摘要：
[Objective] This paper reviews literature on text sentiment analysis, aiming to summarize its technical development trends and applications. [Coverage] We searched relevant literature from the Web of Science Core Collection and CNKI database on the concepts, methods and techniques of sentiment analysis. A total of 69 papers were retrieved from 2011 to 2020 and then analyzed. [Methods] We summarized the main models and applications of text sentiment analysis from the dimensions of time and theme. We also discussed the fields needs to be improved. [Results] There were mainly three methods for text sentiment analysis, which were based on sentiment lexicon and rules, machine learning, as well as deep learning. Each method has advantages and disadvantages. The methods based on multi-strategy hybrid became more popular in recent years. [Limitations] We reviewed previous literature on text sentiment analysis from the perspective of macro-technical methods. More research is needed to compare and elaborate the technical details of sentiment analysis algorithms. [Conclusions] The development of artificial intelligence technology (big data and deep learning) will further improve text sentiment analysis, and benefit business decision making applications. © 2021 Chinese Academy of Sciences. All rights reserved.
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页码：1 / 13
页数：12
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