Sentiment Tech: Exploring the Tools Shaping Emotional Analysis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2474-1_4
Reference13 articles.
1. Bairam M, Abdullah A, Aqlan Q, Manjula B, Lakshman Naik R (2019) A study of sentiment analysis: concepts, techniques, and challenges, vol 28, pp 147–162. Springer. https://doi.org/10.1007/978-981-13-6459-4_16
2. Dolianiti F, Iakovakis D, Dias SB et al (2018) Sentiment analysis techniques and applications in education: a survey. In: International conference on technology and innovation in learning, teaching and education. Springer. Accessed 28 Jan 2024. [Online]. Available: https://doi.org/10.1007/978-3-030-20954-4_31
3. Shaik T et al (2022) A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access 10:56720–56739. https://doi.org/10.1109/ACCESS.2022.3177752
4. Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4). https://doi.org/10.1002/WIDM.1253
5. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J. Accessed 28 Jan 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2090447914000550
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