[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120772-en":3,"doc-seo-120772-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120772,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Detection of emotion by text analysis using machine learning - Research paper","Emotion is central to human experience and is often communicated through text or speech, making automatic recognition a key challenge in human-machine interaction. This study develops an AI approach for automatically detecting emotions from text, supporting systems such as chatbots to assess emotional state and adapt communication. Experiments compare lexicon-based methods and classic machine learning models (Naïve Bayes, SVM) with deep learning neural networks using six emotion classes, and the best neural model is validated via a web application and chatbot interaction. Results show strong multi-class performance and highlight remaining limits in full automation.","TYPE Original Research PUBLISHED 20 September 2023 DOI 10.3389/fpsyg.2023.1190326  \nOPEN ACCESS  \nEDITED BY  \nAlejandro Garcia Ramirez,  \nUniversidade do Vale do Itajaí, Brazil  \nREVIEWED BY  \nMarcelo Gitirana Gomes-Ferreira, Santa Catarina State University, Brazil Ebrahim Ghaderpour,  \nSapienza University of Rome, Italy  \n*CORRESPONDENCE  \nKristína Machová  \n [kristina.machova@tuke.sk](kristina.machova@tuke.sk)  \nRECEIVED 20 March 2023  \nACCEPTED 04 September 2023  \nPUBLISHED 20 September 2023  \nCITATION  \nMachová K, Szabóova M, Paralič J and Mičko J (2023) Detection of emotion by text analysis using machine learning.  \nFront. Psychol. 14:1190326 .  \ndoi: 10.3389/fpsyg.2023.1190326  \nCOPYRIGHT  \n© 2023 Machová, Szabóova, Paralič and Mičko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDetection of emotion by text analysis using machine learning  \nKristína Machová 1*, Martina Szabóova 1, Ján Paralič 1 and Ján Mičko 2  \n1 Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovakia, 2 Department of Social Sciences, Technical University of Košice, Košice, Slovakia  \nEmotions are an integral part of human life. We know many different definitions of emotions. They are most often defined as a complex pattern of reactions, and they could be confused with feelings or moods. They are the way in which individuals cope with matters or situations that they find personally significant. Emotion can also be characterized as a conscious mental reaction (such as anger or fear) subjectively experienced as a strong feeling, usually directed at a specific object. Emotions can be communicated in different ways. Understanding the emotions conveyed in a text or speech of a human by a machine is one of the challenges in the field of human-machine interaction. The article proposes the artificial intelligence approach to automatically detect human emotions, enabling a machine (i.e., a chatbot) to accurately assess emotional state of a human and to adapt its communication accordingly. A complete automation of this process is still a problem. This gap can be filled with machine learning approaches based on automatic learning from experiences represented by the text data from conversations. We conducted experiments with a lexicon-based approach and classic methods of machine learning, appropriate for text processing, such as Naïve Bayes (NB), support vector machine (SVM) and with deep learning using neural networks (NN) to develop a model for detecting emotions in a text. We have compared these models’ effectiveness. The NN detection model performed particularly well in a multi-classification task involving six emotions from the text data. It achieved an F1-score = 0.95 for sadness, among other high scores for other emotions. We also verified the best model in use through a web application and in a Chatbot communication with a human. We created a web application based on our detection model that can analyze a text input by web user and detect emotions expressed in a text of a post or a comment. The model for emotions detection was used also to improve the communication of the Chatbot with a human since the Chatbot has the information about emotional state of a human during communication. Our research demonstrates the potential of machine learning approaches to detect emotions from a text and improve human-machine interaction. However, it is important to note that full automation of an emotion detection is still an open research question, and further work is needed","cbCaiuO1VPwDWmwY","https://ap.wps.com/l/cbCaiuO1VPwDWmwY","pdf",1653453,1,14,"English","en",105,"# 1. Introduction\n## Emotions and definitions\n## Emotion models and emotionality\n# 2. Methods\n## Lexicon-based approach\n## Classic machine learning (Naïve Bayes, SVM)\n## Deep learning with neural networks\n# 3. Experiments and Results\n## Multi-class emotion classification\n## Model performance comparison\n# 4. Application and Validation\n## Web application\n## Chatbot communication\n# 5. Discussion\n## Remaining challenges for automation","[{\"question\":\"How does the article detect emotions from text?\",\"answer\":\"It proposes an AI approach that learns from conversation text data to automatically detect emotions. Models include lexicon-based methods, classic machine learning (Naïve Bayes, SVM), and deep learning neural networks.\"},{\"question\":\"Which models were compared, and what was the best performing one?\",\"answer\":\"The study compares lexicon-based and classic machine learning methods with neural networks. The neural network performed particularly well in multi-class classification across six emotions, achieving an F1-score of 0.95 for sadness.\"},{\"question\":\"How was the best model validated in practice?\",\"answer\":\"The best model was tested through a web application that analyzes user-entered text for emotions and integrated into chatbot communication to improve interaction by using predicted emotional state.\"}]","Detection of emotion by text analysis using machine learning - Research paper | PDF",1785731951,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detection-of-emotion-by-text-analysis-using-machine-learning-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detection-of-emotion-by-text-analysis-using-machine-learning-research-paper/120772/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the article detect emotions from text?","Question",{"text":75,"@type":76},"It proposes an AI approach that learns from conversation text data to automatically detect emotions. Models include lexicon-based methods, classic machine learning (Naïve Bayes, SVM), and deep learning neural networks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models were compared, and what was the best performing one?",{"text":80,"@type":76},"The study compares lexicon-based and classic machine learning methods with neural networks. 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