Sentiment analysis using deep learning approaches: an overview
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2019/12/26
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Indian UGC (journal)
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Refrences185
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Citations56
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Olivier Habimana
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Yuhua Li
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Ruixuan Li
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Xiwu Gu
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Ge Yu
Cite
Habimana, Olivier, et al. “Sentiment Analysis Using Deep Learning Approaches: An Overview”. Science China Information Sciences, vol. 63, no. 1, 2019, https://doi.org/10.1007/s11432-018-9941-6.
Habimana, O., Li, Y., Li, R., Gu, X., & Yu, G. (2019). Sentiment analysis using deep learning approaches: an overview. Science China Information Sciences, 63(1). https://doi.org/10.1007/s11432-018-9941-6
Habimana, Olivier, Yuhua Li, Ruixuan Li, Xiwu Gu, and Ge Yu. “Sentiment Analysis Using Deep Learning Approaches: An Overview”. Science China Information Sciences 63, no. 1 (2019). https://doi.org/10.1007/s11432-018-9941-6.
Habimana O, Li Y, Li R, Gu X, Yu G. Sentiment analysis using deep learning approaches: an overview. Science China Information Sciences. 2019;63(1).
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors | Mathematical Problems in Engineering |
| 153 | 2018 |
| A Generative Model for category text generation | Information Sciences |
| 92 | 2018 |
| DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network | Information Processing & Management |
| 67 | 2018 |
Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level | Algorithms |
| 8 | 2018 |
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification | Transactions of the Association for Computational Linguistics |
| 56 | 2018 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Multi-temporal dependency handling in video smoke recognition: A holistic approach spanning spatial, short-term, and long-term perspectives | Expert Systems with Applications |
| 2024 | |
| MV-SHIF: Multi-view symmetric hypothesis inference fusion network for emotion-cause pair extraction in documents | Neural Networks |
| 2024 | |
A new framework for assessment of park management in smart cities: a study based on social media data and deep learning | Scientific Reports |
| 2024 | |
Modeling a Novel Approach for Emotion Recognition Using Learning and Natural Language Processing | ACM Transactions on Asian and Low-Resource Language Information Processing |
| 2024 | |
A Survey of Cutting-edge Multimodal Sentiment Analysis | ACM Computing Surveys |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 28
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A comprehensive review on feature set used for anaphora resolution and was published in 2020. The most recent citation comes from a 2024 study titled MV-SHIF: Multi-view symmetric hypothesis inference fusion network for emotion-cause pair extraction in documents. This article reached its peak citation in 2023, with 19 citations. It has been cited in 41 different journals, 19% of which are open access. Among related journals, the Artificial Intelligence Review cited this research the most, with 4 citations. The chart below illustrates the annual citation trends for this article.