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Sentiment Analysis Based on Deep Learning: A Comparative Study
Open accessAbstract
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.
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Cites
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Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
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Pimpalkar, A., & Raj R, J. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117581 · Cited by 98
AI Companions Reduce Loneliness
De Freitas, J., Uğuralp, A. K., Uğuralp, Z., & Puntoni, S. (2024) · doi:10.2139/ssrn.4893097 · Cited by 80
Domain-specific sentiment analysis: An optimized deep learning approach for the financial markets
Yekrangi, M., & Nikolov, N. S. (2023) · IEEE Access · doi:10.1109/access.2023.3293733 · Cited by 14
Metadata
- Authors
- Dang, N. C., Moreno-García, M. N., & De la Prieta, F.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2020-03-14
- Volume
- 9
- Issue
- 3
- Pages
- 483
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3011570378
- Cited by
- 593
- References
- 51
- FWCI
- 27.84
- Type
- journal article
- Language
- en
- Open access
- gold
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