Detecting Emotions behind the Screen
Students’ emotional health is a major contributor to educational success. Hence, to support students’ success in online learning platforms, we contribute with the development of an analysis of the emotional orientations and triggers in their text messages. Such analysis could be automated and used f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/3/4/56 |
| _version_ | 1797461871530344448 |
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| author | Najla Alkaabi Nazar Zaki Heba Ismail Manzoor Khan |
| author_facet | Najla Alkaabi Nazar Zaki Heba Ismail Manzoor Khan |
| author_sort | Najla Alkaabi |
| collection | DOAJ |
| description | Students’ emotional health is a major contributor to educational success. Hence, to support students’ success in online learning platforms, we contribute with the development of an analysis of the emotional orientations and triggers in their text messages. Such analysis could be automated and used for early detection of the emotional status of students. In our approach, we relied on transfer learning to train the model, using the pre-trained Bidirectional Encoder Representations from Transformers model (BERT). The model classified messages as positive, negative, or neutral. The transfer learning model was then used to classify a larger unlabeled dataset and fine-grained emotions in the negative messages only, using NRC lexicon. In our analysis to the results, we focused in discovering the dominant negative emotions expressed and the most common words students used to express them. We believe this can be an important clue or first line of detection that may assist mental health practitioners to develop targeted programs for students, especially with the massive shift to online education due to the COVID-19 pandemic. We compared our model to a state-of-the-art ML-based model and found our model outperformed the other by achieving a 91% accuracy compared to an 86%. To the best of our knowledge, this is the first study to focus on a mental health analysis of students in online educational platforms other than massive open online courses (MOOCs). |
| first_indexed | 2024-03-09T17:25:32Z |
| format | Article |
| id | doaj.art-f7cea5c9390a4d3aaed3efad5a35b792 |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-03-09T17:25:32Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj.art-f7cea5c9390a4d3aaed3efad5a35b7922023-11-24T12:48:51ZengMDPI AGAI2673-26882022-11-013494896010.3390/ai3040056Detecting Emotions behind the ScreenNajla Alkaabi0Nazar Zaki1Heba Ismail2Manzoor Khan3College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab EmiratesCollege of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab EmiratesDepartment of CS-IT, College of Engineering, Abu Dhabi University (ADU), Abu Dhabi 60511, United Arab EmiratesCollege of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab EmiratesStudents’ emotional health is a major contributor to educational success. Hence, to support students’ success in online learning platforms, we contribute with the development of an analysis of the emotional orientations and triggers in their text messages. Such analysis could be automated and used for early detection of the emotional status of students. In our approach, we relied on transfer learning to train the model, using the pre-trained Bidirectional Encoder Representations from Transformers model (BERT). The model classified messages as positive, negative, or neutral. The transfer learning model was then used to classify a larger unlabeled dataset and fine-grained emotions in the negative messages only, using NRC lexicon. In our analysis to the results, we focused in discovering the dominant negative emotions expressed and the most common words students used to express them. We believe this can be an important clue or first line of detection that may assist mental health practitioners to develop targeted programs for students, especially with the massive shift to online education due to the COVID-19 pandemic. We compared our model to a state-of-the-art ML-based model and found our model outperformed the other by achieving a 91% accuracy compared to an 86%. To the best of our knowledge, this is the first study to focus on a mental health analysis of students in online educational platforms other than massive open online courses (MOOCs).https://www.mdpi.com/2673-2688/3/4/56BERTeducationemotions detectionmental healthNRC lexicononline learning |
| spellingShingle | Najla Alkaabi Nazar Zaki Heba Ismail Manzoor Khan Detecting Emotions behind the Screen AI BERT education emotions detection mental health NRC lexicon online learning |
| title | Detecting Emotions behind the Screen |
| title_full | Detecting Emotions behind the Screen |
| title_fullStr | Detecting Emotions behind the Screen |
| title_full_unstemmed | Detecting Emotions behind the Screen |
| title_short | Detecting Emotions behind the Screen |
| title_sort | detecting emotions behind the screen |
| topic | BERT education emotions detection mental health NRC lexicon online learning |
| url | https://www.mdpi.com/2673-2688/3/4/56 |
| work_keys_str_mv | AT najlaalkaabi detectingemotionsbehindthescreen AT nazarzaki detectingemotionsbehindthescreen AT hebaismail detectingemotionsbehindthescreen AT manzoorkhan detectingemotionsbehindthescreen |