The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
Current State of Text Sentiment Analysis from Opinion to Emotion Mining
Abstract
Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.
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Cites
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Opinion mining and sentiment analysis
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Cited by
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Liu, W., Qiu, J.-L., Zheng, W.-L., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3071170 · Cited by 452
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges
Das, R., & Singh, T. D. (2023) · ACM Computing Surveys · doi:10.1145/3586075 · Cited by 363
Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171
Metadata
- Authors
- Yadollahi, A., Shahraki, A. G., & Zaiane, O. R.
- Venue
- ACM Computing Surveys
- Published
- 2017-05-25
- Volume
- 50
- Issue
- 2
- Pages
- 1-33
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2618843390
- Cited by
- 489
- References
- 120
- FWCI
- 24.93
- Type
- journal article
- Language
- en
- Open access
- closed
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