References
[1]. Seyeditabari, A., Tabari, N., & Zadrozny, W. (2018). Emotion Detection in Text: A Review. ArXiv print,674.
[2]. Tunca, S., Sezen, B., & Wilk, V. (2023). An exploratory content and sentiment analysis of the guardian metaverse articles using leximancer and natural language processing. Journal of Big Data, 10(1), 82.
[3]. Kaltenbrunner, A., & Gómez, V. (2021). Uncovering the Limits of Text-based Emotion Detection. ArXiv print,1900.
[4]. Deng, J., & Ren, F. (2021). A survey of textual emotion recognition and its challenges. IEEE Transactions on Affective Computing, 14(1), 49-67.
[5]. Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social network analysis and mining, 11(1), 81.
[6]. Prakash, T. N., & Aloysius, A. (2021). Textual sentiment analysis using lexicon based approaches. Annals of the Romanian Society for Cell Biology, 25(4), 9878-9885.
[7]. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
[8]. Wang, P., Zhou, Q., Wu, Y. (2024). DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis. ArXiv print, 12225.
[9]. Etelis, I., Rosenfeld, A., Weinberg, A. I., & Sarne, D. (2024). Generating Effective Ensembles for Sentiment Analysis. ArXiv print:16700.
[10]. Igali, A., Abdrakhman, A., Torekhan, Y. (2024). Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis. ArXiv print,1838.
[11]. Gunasekaran, K. P. (2023). Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review. ArXiv print, 14842.
[12]. Jim, J. R., Talukder, M. A. R., Malakar, P. (2024). Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 6(100059), 2949-7191.
[13]. Li, Y., Chan, J., Peko, G., & Sundaram, D. (2023). Mixed emotion extraction analysis and visualisation of social media text. Data & Knowledge Engineering, 148(102220).
[14]. Miriam, F., Curry, A., Curry, A. C. (2024). Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions. ArXiv print, 1222.
[15]. Kusal, S., Patil, S., Choudrie, J. (2022). A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions. ArXiv print, 3235.
Cite this article
Miao,P. (2025). Analysis of the Technologies and Methods of Textual Emotion Analysis. Applied and Computational Engineering,162,112-121.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Seyeditabari, A., Tabari, N., & Zadrozny, W. (2018). Emotion Detection in Text: A Review. ArXiv print,674.
[2]. Tunca, S., Sezen, B., & Wilk, V. (2023). An exploratory content and sentiment analysis of the guardian metaverse articles using leximancer and natural language processing. Journal of Big Data, 10(1), 82.
[3]. Kaltenbrunner, A., & Gómez, V. (2021). Uncovering the Limits of Text-based Emotion Detection. ArXiv print,1900.
[4]. Deng, J., & Ren, F. (2021). A survey of textual emotion recognition and its challenges. IEEE Transactions on Affective Computing, 14(1), 49-67.
[5]. Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social network analysis and mining, 11(1), 81.
[6]. Prakash, T. N., & Aloysius, A. (2021). Textual sentiment analysis using lexicon based approaches. Annals of the Romanian Society for Cell Biology, 25(4), 9878-9885.
[7]. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
[8]. Wang, P., Zhou, Q., Wu, Y. (2024). DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis. ArXiv print, 12225.
[9]. Etelis, I., Rosenfeld, A., Weinberg, A. I., & Sarne, D. (2024). Generating Effective Ensembles for Sentiment Analysis. ArXiv print:16700.
[10]. Igali, A., Abdrakhman, A., Torekhan, Y. (2024). Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis. ArXiv print,1838.
[11]. Gunasekaran, K. P. (2023). Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review. ArXiv print, 14842.
[12]. Jim, J. R., Talukder, M. A. R., Malakar, P. (2024). Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 6(100059), 2949-7191.
[13]. Li, Y., Chan, J., Peko, G., & Sundaram, D. (2023). Mixed emotion extraction analysis and visualisation of social media text. Data & Knowledge Engineering, 148(102220).
[14]. Miriam, F., Curry, A., Curry, A. C. (2024). Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions. ArXiv print, 1222.
[15]. Kusal, S., Patil, S., Choudrie, J. (2022). A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions. ArXiv print, 3235.