Determining someone's emotional state from a written text might be challenging, but it's crucial because textual expressions often transcend the use of emotion terminology. They emerge from the way concepts are seen and relate to one another inside the text. Text sentiment recognition is a critical skill for human-computer interaction. Text-based emotion recognition still requires further research, despite significant progress in speech and facial expression emotion detection. expressions and movements. In essence, emotion detection in text texts is a content-based classification task that incorporates ideas from machine learning and natural language processing. The methods for emotion detection and recognition based on textual data are covered in this study.

Keywords:Textual emotion detection, emotion word ontology, human-computer interaction, tweet

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1. Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S. Emotion recognition in human- computer interaction, IEEE Signal Process 2001 Jan;18(1): 32–80. doi: 10.1109/79.911197

2. Parrott Emotions in social psychology. Philadelphia: Psychology Press; 2001.
3. Maaoui C., Pruski A, Abdat F. Emotion recognition for human machine communication. Proc IEEE/RSJ Int Conf Intel Robots Systems (IROS 08), IEEE Comp Soc, 2008 Sep; 1210–1215. doi: 1109/IROS.2008.4650870
4. Kao EC, Liu CC, Yang TH, Hsieh CT, Soo VW. Towards text-based emotion detection a survey and possible improvements. In 2009 International Conference on Information Management and IEEE. 2009 Apr 3: 70–74.
5. Fragopanagos N, Taylor JG. Emotion recognition in human-computer interaction. Department of Mathematics, King's College, Strand, London WC2 R2LS, UK Neural Networks 2005 March:389–405.
6. Silveira RA, Da Silva GK, Bitencourt T, Gelaim A, Marchi J, De La Prieta F. Towards a model of open and reliable cognitive multiagent systems: Dealing with trust and ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 2015;4(3):57–86.
7. Wu L, Chuang CH, Hsu CH. Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange Int J Prod Econ. 2014 Feb 1;148:122–32.
8. Teng Z, Ren F, Kuroiwa S. Recognition of emotion with SVMs. Lecture notes of artificial intelligence 4114, -S. Huang, K. Li, and G. W. Irwin, Eds. Berlin Heidelberg: Springer;2006:701–710. doi: 10.1007/11816171_87.
9. Yang C, Lin KH-Y, Chen H-H. Emotion classification using web blog Proc. IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, 2007 Nov.:275–278. doi: 10.1109/WI.2007.50.
10. Lee CM, Narayanan SS, Pieraccini R. Combining acoustic and language information for emotion Proc. 7th International Conference on Spoken Language Processing (ICSLP02). 2002:873–876.
11. Wu C-H, Chuang Z-J, Lin Y-C. Emotion recognition from text using semantic labels and separable mixture ACM Transactions on Asian Language Information Processing (TALIP). 2006 Jun;5(2): 165–183. doi:10.1145/1165255.1165259.
12. Elliott C. The affective reasoner: A process model of emotions in a multiagent system. Doctoral thesis, Northwestern University, Evanston, IL, 1992
13. Manjula V, Mahin A. E-assistant for paralyzed patients using IOT. Int Adv Res Sci Tech. 2023 April;3(7):115–116.
14. Rubin DL. Creating and curating a terminology for radiology: Ontology modeling and analysis. J Digi 2008 Dec;21:355–62.
15. Sebe N, Cohen I, Gevers T, Huang Multimodal approaches for emotion recognition: A survey. Internet Imaging VI. SPIE. 2005 Jan 17;5670: 56–67.
16. Cristani M, Cuel A survey on ontology creation methodologies. International Journal on Semantic Web and Information Systems (IJSWIS). 2005 Apr 1;1(2):49–69.

Determining someone's emotional state from a written text might be challenging, but it's crucial because textual expressions often transcend the use of emotion terminology. They emerge from the way concepts are seen and relate to one another inside the text. Text sentiment recognition is a critical skill for human-computer interaction. Text-based emotion recognition still requires further research, despite significant progress in speech and facial expression emotion detection. expressions and movements. In essence, emotion detection in text texts is a content-based classification task that incorporates ideas from machine learning and natural language processing. The methods for emotion detection and recognition based on textual data are covered in this study.

Keywords:Textual emotion detection, emotion word ontology, human-computer interaction, tweet

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S. Emotion recognition in human- computer interaction, IEEE Signal Process 2001 Jan;18(1): 32–80. doi: 10.1109/79.911197

2. Parrott Emotions in social psychology. Philadelphia: Psychology Press; 2001.
3. Maaoui C., Pruski A, Abdat F. Emotion recognition for human machine communication. Proc IEEE/RSJ Int Conf Intel Robots Systems (IROS 08), IEEE Comp Soc, 2008 Sep; 1210–1215. doi: 1109/IROS.2008.4650870
4. Kao EC, Liu CC, Yang TH, Hsieh CT, Soo VW. Towards text-based emotion detection a survey and possible improvements. In 2009 International Conference on Information Management and IEEE. 2009 Apr 3: 70–74.
5. Fragopanagos N, Taylor JG. Emotion recognition in human-computer interaction. Department of Mathematics, King's College, Strand, London WC2 R2LS, UK Neural Networks 2005 March:389–405.
6. Silveira RA, Da Silva GK, Bitencourt T, Gelaim A, Marchi J, De La Prieta F. Towards a model of open and reliable cognitive multiagent systems: Dealing with trust and ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 2015;4(3):57–86.
7. Wu L, Chuang CH, Hsu CH. Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange Int J Prod Econ. 2014 Feb 1;148:122–32.
8. Teng Z, Ren F, Kuroiwa S. Recognition of emotion with SVMs. Lecture notes of artificial intelligence 4114, -S. Huang, K. Li, and G. W. Irwin, Eds. Berlin Heidelberg: Springer;2006:701–710. doi: 10.1007/11816171_87.
9. Yang C, Lin KH-Y, Chen H-H. Emotion classification using web blog Proc. IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, 2007 Nov.:275–278. doi: 10.1109/WI.2007.50.
10. Lee CM, Narayanan SS, Pieraccini R. Combining acoustic and language information for emotion Proc. 7th International Conference on Spoken Language Processing (ICSLP02). 2002:873–876.
11. Wu C-H, Chuang Z-J, Lin Y-C. Emotion recognition from text using semantic labels and separable mixture ACM Transactions on Asian Language Information Processing (TALIP). 2006 Jun;5(2): 165–183. doi:10.1145/1165255.1165259.
12. Elliott C. The affective reasoner: A process model of emotions in a multiagent system. Doctoral thesis, Northwestern University, Evanston, IL, 1992
13. Manjula V, Mahin A. E-assistant for paralyzed patients using IOT. Int Adv Res Sci Tech. 2023 April;3(7):115–116.
14. Rubin DL. Creating and curating a terminology for radiology: Ontology modeling and analysis. J Digi 2008 Dec;21:355–62.
15. Sebe N, Cohen I, Gevers T, Huang Multimodal approaches for emotion recognition: A survey. Internet Imaging VI. SPIE. 2005 Jan 17;5670: 56–67.
16. Cristani M, Cuel A survey on ontology creation methodologies. International Journal on Semantic Web and Information Systems (IJSWIS). 2005 Apr 1;1(2):49–69.