Text emotional analysis in Natural Language Processing

Research Article
Open access

Text emotional analysis in Natural Language Processing

Shangyue Lin 1*
  • 1 Hangzhou No.2 High School of Zhe Jiang Province    
  • *corresponding author 220712031@njnu.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/36/20230440
ACE Vol.36
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-297-8
ISBN (Online): 978-1-83558-298-5

Abstract

Natural Language Processing (NLP) is an important research direction in the field of artificial intelligence, aimed at enabling computers to understand and process human natural language. Emotional analysis refers to the use of computer technology to automatically recognize and classify the emotional tendencies expressed in text, such as positive, negative, or neutral. This technology can be applied to fields such as social media analysis, public opinion monitoring, market research, etc. The goal is to enable computers to understand and interpret emotional information in text like humans. Text sentiment analysis, as a key task of NLP, aims to identify and classify emotional tendencies expressed in text. This article will delve into the field of text sentiment analysis in natural language processing, focusing on the role and application of standard sentiment dictionaries and corpora in sentiment analysis. Reviewing relevant research conclusions, providing reference and inspiration for achieving more accurate and comprehensive text sentiment analysis through research and exploration.

Keywords:

Natural Language Processing, Artificial Intelligence, Emotional Analysis

Lin,S. (2024). Text emotional analysis in Natural Language Processing. Applied and Computational Engineering,36,163-172.
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References

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[2]. Tanana M J, Soma C S, Kuo P B, et al. 2021 How do you feel? Using natural language processing to automatically rate emotion in psychotherapy[J]. Behav. Res. Methods (13):1-14

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[4]. Darwish K, Habash N, Abbas M, et al. 2021 A panoramic survey of natural language processing in the Arab world[J]. CMR 64(4): 72-81

[5]. Wignell P, Chai K, Tan S, et al. 2021 Natural language understanding and multimodal discourse analysis for interpreting extremist communications and the re-use of these materials online[J]. TPV 33(1): 71-95

[6]. Zhang T, Schoene A M, Ji S, et al. 2022 Natural language processing applied to mental illness detection: a narrative review[J]. NPJ Digit Med 5(1): 46

[7]. Peng S, Cao L, Zhou Y, et al. 2022 A survey on deep learning for textual emotion analysis in social networks[J]. DICON 8(5): 745-762

[8]. Khyani D, Siddhartha B S, Niveditha N M, et al. 2021 An interpretation of lemmatization and stemming in natural language processing[J]. J. Uni. Shanghai. Sci. Tech. 22(10): 350-357

[9]. Murthy A R, Kumar K M A. 2021 A review of different approaches for detecting emotion from text[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 1110(1): 012009

[10]. Torregrosa J, Bello-Orgaz G, Martínez-Cámara E, et al. 2023 A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges[J]. J. Ambient Intell. Hum. Comput. 14(8): 9869-9905

[11]. Zad S, Heidari M, James Jr H, et al. 2021 Emotion detection of textual data: An interdisciplinary survey[C]//2021 IEEE World AI IoT Congress (AIIoT). IEEE, 0255-0261

[12]. Fan C, Yuan C, Gui L, et al. 2021 Multi-task sequence tagging for emotion-cause pair extraction via tag distribution refinement[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29: 2339-2350

[13]. Acheampong F A, Nunoo-Mensah H, Chen W. 2021 Transformer models for text-based emotion detection: a review of BERT-based approaches[J]. ARTIF INTELL REV (09): 1-41

[14]. Chen Q, Leaman R, Allot A, et al. 2021 Artificial intelligence in action: addressing the COVID-19 pandemic with natural language processing[J]. Ann. Rev. Biomed. Da. S. (04): 313-339

[15]. Levis M, Westgate C L, Gui J, et al. 2021 Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models[J]. PSYCHOL MED 51(8): 1382-1391

[16]. Khanbhai M, Anyadi P, Symons J, et al. 2021 Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review[J]. BMJ HEALTH CARE INFO 28(1):112-113

[17]. Nandwani P, Verma R. 2021 A review on sentiment analysis and emotion detection from text[J]. SNAM 11(1): 81-81

[18]. Sean B. Findings of the shared task on Emotion Analysis in Tamil[C]//Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. 2022,(13): 279-285

[19]. Pérez J M, Furman D A, Alemany L A, et al. 2021 Robertuito: a pre-trained language model for social media text in spanish[J]. arXiv preprint arXiv.(96):87-88

[20]. Choudrie J, Patil S, Kotecha K, et al. 2021 Applying and understanding an advanced, novel deep learning approach: A Covid 19, text based, emotions analysis study[J]. ISF (23): 1431-1465

[21]. Nandwani P, Verma R. 2021 A review on sentiment analysis and emotion detection from text[J]. SNAM 11(1): 81

[22]. Sean B. Findings of the shared task on Emotion Analysis in Tamil[C]//Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. 2022(13): 279-285

[23]. Graterol W, Diaz-Amado J, Cardinale Y, et al. 2021 Emotion detection for social robots based on NLP transformers and an emotion ontology[J]. Sensors, 21(4): 1322

[24]. Bhowmik N R, Arifuzzaman M, Mondal M R H, et al. 2021 Bangla text sentiment analysis using supervised machine learning with extended lexicon dictionary[J]. NLPR 1(3-4): 34-45

[25]. Harrison C J, Sidey-Gibbons C J. 2021 Machine learning in medicine: a practical introduction to natural language processing[J]. BMC MED RES METHODOL 21(1): 1-11

Cite this article

Lin,S. (2024). Text emotional analysis in Natural Language Processing. Applied and Computational Engineering,36,163-172.

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 the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-297-8(Print) / 978-1-83558-298-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.36
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Nijhawan T, Attigeri G, Ananthakrishna T. 2022 Stress detection using natural language processing and machine learning over social interactions. J. Big D. 9(1): 1-24

[2]. Tanana M J, Soma C S, Kuo P B, et al. 2021 How do you feel? Using natural language processing to automatically rate emotion in psychotherapy[J]. Behav. Res. Methods (13):1-14

[3]. Maulud D H, Zeebaree S R M, Jacksi K, et al. 2021 State of art for semantic analysis of natural language processing[J]. QAJ 1(2): 21-28

[4]. Darwish K, Habash N, Abbas M, et al. 2021 A panoramic survey of natural language processing in the Arab world[J]. CMR 64(4): 72-81

[5]. Wignell P, Chai K, Tan S, et al. 2021 Natural language understanding and multimodal discourse analysis for interpreting extremist communications and the re-use of these materials online[J]. TPV 33(1): 71-95

[6]. Zhang T, Schoene A M, Ji S, et al. 2022 Natural language processing applied to mental illness detection: a narrative review[J]. NPJ Digit Med 5(1): 46

[7]. Peng S, Cao L, Zhou Y, et al. 2022 A survey on deep learning for textual emotion analysis in social networks[J]. DICON 8(5): 745-762

[8]. Khyani D, Siddhartha B S, Niveditha N M, et al. 2021 An interpretation of lemmatization and stemming in natural language processing[J]. J. Uni. Shanghai. Sci. Tech. 22(10): 350-357

[9]. Murthy A R, Kumar K M A. 2021 A review of different approaches for detecting emotion from text[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 1110(1): 012009

[10]. Torregrosa J, Bello-Orgaz G, Martínez-Cámara E, et al. 2023 A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges[J]. J. Ambient Intell. Hum. Comput. 14(8): 9869-9905

[11]. Zad S, Heidari M, James Jr H, et al. 2021 Emotion detection of textual data: An interdisciplinary survey[C]//2021 IEEE World AI IoT Congress (AIIoT). IEEE, 0255-0261

[12]. Fan C, Yuan C, Gui L, et al. 2021 Multi-task sequence tagging for emotion-cause pair extraction via tag distribution refinement[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29: 2339-2350

[13]. Acheampong F A, Nunoo-Mensah H, Chen W. 2021 Transformer models for text-based emotion detection: a review of BERT-based approaches[J]. ARTIF INTELL REV (09): 1-41

[14]. Chen Q, Leaman R, Allot A, et al. 2021 Artificial intelligence in action: addressing the COVID-19 pandemic with natural language processing[J]. Ann. Rev. Biomed. Da. S. (04): 313-339

[15]. Levis M, Westgate C L, Gui J, et al. 2021 Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models[J]. PSYCHOL MED 51(8): 1382-1391

[16]. Khanbhai M, Anyadi P, Symons J, et al. 2021 Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review[J]. BMJ HEALTH CARE INFO 28(1):112-113

[17]. Nandwani P, Verma R. 2021 A review on sentiment analysis and emotion detection from text[J]. SNAM 11(1): 81-81

[18]. Sean B. Findings of the shared task on Emotion Analysis in Tamil[C]//Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. 2022,(13): 279-285

[19]. Pérez J M, Furman D A, Alemany L A, et al. 2021 Robertuito: a pre-trained language model for social media text in spanish[J]. arXiv preprint arXiv.(96):87-88

[20]. Choudrie J, Patil S, Kotecha K, et al. 2021 Applying and understanding an advanced, novel deep learning approach: A Covid 19, text based, emotions analysis study[J]. ISF (23): 1431-1465

[21]. Nandwani P, Verma R. 2021 A review on sentiment analysis and emotion detection from text[J]. SNAM 11(1): 81

[22]. Sean B. Findings of the shared task on Emotion Analysis in Tamil[C]//Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. 2022(13): 279-285

[23]. Graterol W, Diaz-Amado J, Cardinale Y, et al. 2021 Emotion detection for social robots based on NLP transformers and an emotion ontology[J]. Sensors, 21(4): 1322

[24]. Bhowmik N R, Arifuzzaman M, Mondal M R H, et al. 2021 Bangla text sentiment analysis using supervised machine learning with extended lexicon dictionary[J]. NLPR 1(3-4): 34-45

[25]. Harrison C J, Sidey-Gibbons C J. 2021 Machine learning in medicine: a practical introduction to natural language processing[J]. BMC MED RES METHODOL 21(1): 1-11

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