On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence
Abstract Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-06-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00234-5 |
| _version_ | 1827916687961227264 |
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| author | Madiha Tahir Zahid Halim Muhmmad Waqas Shanshan Tu |
| author_facet | Madiha Tahir Zahid Halim Muhmmad Waqas Shanshan Tu |
| author_sort | Madiha Tahir |
| collection | DOAJ |
| description | Abstract Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results show that when English text is translated to French, classification accuracy is higher than others, i.e., 99.04%. Whereas, when the same is translated to Chinese language, its detection rate is lowest among target languages. It is concluded that emotions remain preserved after translation to some extent. Framework consists of TFIDF features. PCA and Discriminant Analysis perform good to detect emotions from translated data. |
| first_indexed | 2024-03-13T03:18:47Z |
| format | Article |
| id | doaj.art-0b5935b05cfd4a85b38dc873d2449503 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-13T03:18:47Z |
| publishDate | 2023-06-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-0b5935b05cfd4a85b38dc873d24495032023-06-25T11:27:14ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-06-0116111110.1007/s44196-023-00234-5On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational IntelligenceMadiha Tahir0Zahid Halim1Muhmmad Waqas2Shanshan Tu3The Machine Intelligence Research Group (MInG), Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and TechnologyThe Machine Intelligence Research Group (MInG), Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and TechnologySchool of Engineering, Edith Cowan UniversityBeijing University of TechnologyAbstract Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results show that when English text is translated to French, classification accuracy is higher than others, i.e., 99.04%. Whereas, when the same is translated to Chinese language, its detection rate is lowest among target languages. It is concluded that emotions remain preserved after translation to some extent. Framework consists of TFIDF features. PCA and Discriminant Analysis perform good to detect emotions from translated data.https://doi.org/10.1007/s44196-023-00234-5Emotion identificationDynamicMachine analysisTranslated textData mining |
| spellingShingle | Madiha Tahir Zahid Halim Muhmmad Waqas Shanshan Tu On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence International Journal of Computational Intelligence Systems Emotion identification Dynamic Machine analysis Translated text Data mining |
| title | On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence |
| title_full | On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence |
| title_fullStr | On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence |
| title_full_unstemmed | On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence |
| title_short | On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence |
| title_sort | on the effect of emotion identification from limited translated text samples using computational intelligence |
| topic | Emotion identification Dynamic Machine analysis Translated text Data mining |
| url | https://doi.org/10.1007/s44196-023-00234-5 |
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