Analysis of the Technologies and Methods of Textual Emotion Analysis

Research Article
Open access

Analysis of the Technologies and Methods of Textual Emotion Analysis

Pengyuan Miao 1*
  • 1 College of Mathematical Science, Tianjin Normal University, Tianjin, China    
  • *corresponding author miaopy256@stu.tjnu.edu.cn
ACE Vol.162
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-157-0
ISBN (Online): 978-1-80590-158-7

Abstract

This paper reviews the development situation in Textual Emotion Analysis (TEA) and summarises the basic and mainstream methods of textual emotion analysis. This review explores how the researchers usually use these methods, the dataset of relevant research, the core features of these methods and their strengths and weaknesses. Specifically, this paper analyses the traditional and advanced usage modes in textual emotion analysis and provides insights into the recent relevant research and its results. Moreover, this paper points out the current challenges of textual emotion analysis, its possible solution and the future researches of textual emotion analysis. This paper fills an essential aspect of textual emotion analysis by summarizing and describing textual emotion analysis methodologies in detail, which can help the research in textual emotion analysis. The language structure of humans has become increasingly complex, which results in the challenges of textual emotion analysis. The standard and simple methods cannot provide a great solution to the current challenges. It is essential to understand the development situation and prospects of textual emotion analysis, as well as its creative methods the core features and effective usage mode in textual emotion analysis.

Keywords:

Textual Emotion Analysis, Core Features, Usage Mode, Current Challenges

Miao,P. (2025). Analysis of the Technologies and Methods of Textual Emotion Analysis. Applied and Computational Engineering,162,112-121.
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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.


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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About volume

Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN:978-1-80590-157-0(Print) / 978-1-80590-158-7(Online)
Editor:Anil Fernando
Conference date: 24 October 2025
Series: Applied and Computational Engineering
Volume number: Vol.162
ISSN:2755-2721(Print) / 2755-273X(Online)

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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.