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Conference Paper: Detecting Emotional Distress from Text
Title | Detecting Emotional Distress from Text |
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Authors | |
Issue Date | 2021 |
Publisher | Association for Information Systems. The Proceedings' web site is located at http://aisel.aisnet.org/amcis2021/ |
Citation | Proceedings of the 27th Americas Conference on Information Systems (AMCIS 2021): Digital Innovation and Entrepreneurship, Virtual Conference, 9-13 August 2021, no. 8 How to Cite? |
Abstract | Emotional distress, such as depression, has become a significant problem in modern societies. Previous research has proposed machine learning models to automatically detect emotional distress from online social media texts. However, these approaches have not effectively incorporated domain knowledge into state-of-the-art architectures and have not been tested on writing in a more private context. This paper discusses our proposed plan to address these two research gaps. First, we will design and evaluate a deep learning model that incorporates domain knowledge to detect emotional distress from texts. Second, we will collect texts from both social media platforms and private diary writing to study the differences in the classification performance of the proposed model on these texts. |
Description | Session: Artificial Intelligence and Semantic Technologies for Intelligence Systems (SIG ODIS) - Emergent Research Forum (ERF) papers - Paper Number -1310 |
Persistent Identifier | http://hdl.handle.net/10722/304107 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Chau, MCL | - |
dc.contributor.author | Chao, MM | - |
dc.contributor.author | Liu, W | - |
dc.date.accessioned | 2021-09-23T08:55:20Z | - |
dc.date.available | 2021-09-23T08:55:20Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Proceedings of the 27th Americas Conference on Information Systems (AMCIS 2021): Digital Innovation and Entrepreneurship, Virtual Conference, 9-13 August 2021, no. 8 | - |
dc.identifier.isbn | 9781733632584 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304107 | - |
dc.description | Session: Artificial Intelligence and Semantic Technologies for Intelligence Systems (SIG ODIS) - Emergent Research Forum (ERF) papers - Paper Number -1310 | - |
dc.description.abstract | Emotional distress, such as depression, has become a significant problem in modern societies. Previous research has proposed machine learning models to automatically detect emotional distress from online social media texts. However, these approaches have not effectively incorporated domain knowledge into state-of-the-art architectures and have not been tested on writing in a more private context. This paper discusses our proposed plan to address these two research gaps. First, we will design and evaluate a deep learning model that incorporates domain knowledge to detect emotional distress from texts. Second, we will collect texts from both social media platforms and private diary writing to study the differences in the classification performance of the proposed model on these texts. | - |
dc.language | eng | - |
dc.publisher | Association for Information Systems. The Proceedings' web site is located at http://aisel.aisnet.org/amcis2021/ | - |
dc.relation.ispartof | Proceedings of the Americas Conference on Information Systems (AMCIS 2021) | - |
dc.title | Detecting Emotional Distress from Text | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Chau, MCL: mchau@business.hku.hk | - |
dc.identifier.authority | Chau, MCL=rp01051 | - |
dc.identifier.hkuros | 325376 | - |
dc.publisher.place | Montreal, Canada | - |