Sentiment analysis using deep learning architectures: a review
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Social media data, company news, and trend analysis can classify investors' stock sentiments as positive, negative, or neutral.
“Social media data, company news, and trend analysis can classify investors' stock sentiments as positive, negative, or neutral (Yadav and Vishwakarma 2019).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Yadav and Vishwakarma 2019
Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.01.095 · Cited by 534 · see citing works
MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos
Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · arXiv · doi:10.48550/arxiv.1606.06259 · Cited by 350 · see citing works
Multimodal sentiment analysis with word-level fusion and reinforcement learning
Chen, M., Wang, S., Liang, P. P., Baltrušaitis, T., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3136801 · Cited by 334 · see citing works
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Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023) · International Journal of Financial Studies · doi:10.3390/ijfs11030094 · Cited by 262 · see citing works
“Social media data, company news, and trend analysis can classify investors’ stock sentiments as positive, negative, or neutral (Yadav and Vishwakarma 2019).”
BackgroundMachine Learning approaches in stock price prediction: A systematic review
Soni, P., Tewari, Y., & Krishnan, D. (2022) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/2161/1/012065 · Cited by 132 · see citing works
Benchmark Data Contamination of Large Language Models: A Survey
Xu, C., Guan, S., Greene, D., & Kechadi, M.-T. (2024) · arXiv · doi:10.48550/arxiv.2406.04244 · Cited by 22 · see citing works
ChatGPT identifies gender disparities in scientific peer review
Verharen, J. P. (2023) · eLife · doi:10.7554/elife.90230 · Cited by 16 · see citing works
Gamal, D., Alfonse, M., Jiménez-Zafra, S. M., & Aref, M. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7020058 · Cited by 9 · see citing works
- Authors
- Yadav, A., & Vishwakarma, D. K.
- Affiliations
- Delhi Technological University (IN)
- Corresponding author
- Yadav, Ashima
- Vishwakarma, Dinesh Kumar
- Venue
- Artificial Intelligence Review
- Published
- 2019-12-02
- Volume
- 53
- Issue
- 6
- Pages
- 4335-4385
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2993843842
- Cited by
- 841
- References
- 5
- Citations by year
- 2026: 56 · 2025: 132 · 2024: 160 · 2023: 196 · 2022: 159 · 2021: 107 · 2020: 31
- FWCI
- 34.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- sentiment analysis, deep learning architectures, social media, multilingual sentiment analysis, sentiment datasets, unstructured text
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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