Review of Methods and Applications of Text Sentiment Analysis
Zhong Jiawa1,2,Liu Wei1(),Wang Sili1,Yang Heng1
1Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 2School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
[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.
钟佳娃,刘巍,王思丽,杨恒. 文本情感分析方法及应用综述*[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis. Data Analysis and Knowledge Discovery, 2021, 5(6): 1-13.
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