Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10146 |
| _version_ | 1827678289676730368 |
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| author | Artur Zygadło Marek Kozłowski Artur Janicki |
| author_facet | Artur Zygadło Marek Kozłowski Artur Janicki |
| author_sort | Artur Zygadło |
| collection | DOAJ |
| description | In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>90</mn><mo>%</mo></mrow></semantics></math></inline-formula> for sentiment (3-class) classification and almost <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>80</mn><mo>%</mo></mrow></semantics></math></inline-formula> for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT. |
| first_indexed | 2024-03-10T06:06:15Z |
| format | Article |
| id | doaj.art-2367ffc32566422687116e31ad3d0359 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:15Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2367ffc32566422687116e31ad3d03592023-11-22T20:28:29ZengMDPI AGApplied Sciences2076-34172021-10-0111211014610.3390/app112110146Text-Based Emotion Recognition in English and Polish for Therapeutic ChatbotArtur Zygadło0Marek Kozłowski1Artur Janicki2Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, PolandFaculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, PolandFaculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, PolandIn this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>90</mn><mo>%</mo></mrow></semantics></math></inline-formula> for sentiment (3-class) classification and almost <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>80</mn><mo>%</mo></mrow></semantics></math></inline-formula> for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT.https://www.mdpi.com/2076-3417/11/21/10146human-machine interactionchatbotsentiment recognitionemotion recognitionPolish languageparallel text corpus |
| spellingShingle | Artur Zygadło Marek Kozłowski Artur Janicki Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot Applied Sciences human-machine interaction chatbot sentiment recognition emotion recognition Polish language parallel text corpus |
| title | Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot |
| title_full | Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot |
| title_fullStr | Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot |
| title_full_unstemmed | Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot |
| title_short | Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot |
| title_sort | text based emotion recognition in english and polish for therapeutic chatbot |
| topic | human-machine interaction chatbot sentiment recognition emotion recognition Polish language parallel text corpus |
| url | https://www.mdpi.com/2076-3417/11/21/10146 |
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