Fine-grained Sentiment Classification
Fine-Grained Sentiment Classification is a sentiment analysis task that categorizes text into highly specific sentiment levels, such as very negative, negative, neutral, positive, and very positive, thereby distinguishing subtle affective variations beyond the broad positive/negative/neutral triad.
318
Publications
31.4K
Citations
985
Authors
395
Institutions
Publications per year
2017–2026
258
Parents
318
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher, Alex Perelygin, Jean Y. Wu et al. · 2013 · 6.8K citations · Full text
Attention-based LSTM for Aspect-level Sentiment Classification
Yequan Wang, Minlie Huang, Xiaoyan Zhu et al. · 2016 · 2.3K citations · Full text
Semantic Compositionality through Recursive Matrix-Vector Spaces
Richard Socher, Brody Huval, Christopher D. Manning et al. · 2012 · 1.3K citations
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model
Jin Wang, Liang Yu, K. Robert Lai et al. · 2016 · 587 citations · Full text
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395
Tsinghua University
Beijing, China
7 h-index
29 publications
8.2K citations
Yuan Ze University
Taoyuan District, Taiwan
6 h-index
14 publications
2.3K citations

Harbin Institute of Technology
Harbin, China
5 h-index
16 publications
906 citations
Wuhan University
Wuhan, China
5 h-index
14 publications
452 citations
Yunnan University
Kunming, China
5 h-index
9 publications
850 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
12 publications
277 citations
Agency for Science, Technology and Research
Singapore, Singapore
4 h-index
9 publications
424 citations

East China Normal University
Shanghai, China
4 h-index
10 publications
788 citations
1–8 of 395
16 publications · 1.3K citations
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Repository · 14 publications · 973 citations
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12 publications · 971 citations
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10 publications · 821 citations
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8 publications · 502 citations
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