期刊
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 13, 期 2, 页码 845-863出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2020.2970399
关键词
Sentiment analysis; Social networking (online); Data mining; Machine learning; Task analysis; Tools; Sun; Aspect; computational linguistic; deep learning; sentiment analysis; sentiment evolution; social media
资金
- National key research and development program in China [2019YFB2102300]
- World-Class Universities (Disciplines) Fund for the Central Universities of China [PY3A022]
- Characteristic Development Guidance Fund for the Central Universities of China [PY3A022]
- Ministry of Education Fund Projects [18JZD022, 2017B00030]
- Shenzhen Science and Technology Project [JCYJ20180306170836595]
- Basic Scientific Research Operating Expenses of Central Universities [ZDYF2017006]
- Xi'an Navinfo Corp. & Engineering Center of Xi'an Intelligence Spatial-temporal Data Analysis Project [C2020103]
- Beilin District of Xi'an Science & Technology Project [GX1803]
This article investigates the issues and challenges in aspect-based sentiment analysis and summarizes recent progress. It also discusses future research directions to assist researchers and improve sentiment classification.
The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analysed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense advancements in this field urged the researchers to devise new techniques and approaches, each sermonizing a different research analysis/question, that cope with upcoming issues and complex scenarios of Aspect-based Sentiment Analysis. Therefore, this survey emphasized on the issues and challenges that are related to extraction of different aspects and their relevant sentiments, relational mapping between aspects, interactions, dependencies, and contextual-semantic relationships between different data objects for improved sentiment accuracy, and prediction of sentiment evolution dynamicity. A rigorous overview of the recent progress is summarized based on whether they contributed towards highlighting and mitigating the issue of Aspect Extraction, Aspect Sentiment Analysis or Sentiment Evolution. The reported performance for each scrutinized study of Aspect Extraction and Aspect Sentiment Analysis is also given, showing the quantitative evaluation of the proposed approach. Future research directions are proposed and discussed, by critically analysing the presented recent solutions, that will be helpful for researchers and beneficial for improving sentiment classification at aspect-level.
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