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|a 10.1007/s10462-022-10386-z
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|a eng
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1 |
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|a Cui, Jingfeng
|e verfasserin
|4 aut
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| 245 |
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|a Survey on sentiment analysis
|b evolution of research methods and topics
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|c 2023
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|a Date Revised 16.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a © The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work
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|a Journal Article
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|a Evolution analysis
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|a Keyword co-occurrence analysis
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| 650 |
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|a Research methods
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| 650 |
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|a Research topics
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| 650 |
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4 |
|a Sentiment analysis
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| 700 |
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|a Wang, Zhaoxia
|e verfasserin
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1 |
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|a Ho, Seng-Beng
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|a Cambria, Erik
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|t Artificial intelligence review
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|g (2023) vom: 06. Jan., Seite 1-42
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|u http://dx.doi.org/10.1007/s10462-022-10386-z
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