Computational Approaches for Emotion Detection in Text
Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information. Recently, interest has been growing among researchers to find ways of detect...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/36906 |
| _version_ | 1848754901161607168 |
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| author | Binali, Haji Wu, Chen Potdar, Vidyasagar |
| author2 | Leila Ismail |
| author_facet | Leila Ismail Binali, Haji Wu, Chen Potdar, Vidyasagar |
| author_sort | Binali, Haji |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper looks at emotion theories that provide a basis for emotion models. It shows how these models have been used by discussing computational approaches to emotion detection. The emotion detection architecture that we propose consists of two components, knowledge based approach and learning systems approach. We present a prototype based on an architecture that we have proposed and demonstrate some of the challenges involved in detecting emotions from text. |
| first_indexed | 2025-11-14T08:47:46Z |
| format | Conference Paper |
| id | curtin-20.500.11937-36906 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:47:46Z |
| publishDate | 2010 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-369062023-01-18T08:46:43Z Computational Approaches for Emotion Detection in Text Binali, Haji Wu, Chen Potdar, Vidyasagar Leila Ismail Elizabeth Chang Achim P Karduck Text classification Sentiment analysis Emotion detection Emotion models Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper looks at emotion theories that provide a basis for emotion models. It shows how these models have been used by discussing computational approaches to emotion detection. The emotion detection architecture that we propose consists of two components, knowledge based approach and learning systems approach. We present a prototype based on an architecture that we have proposed and demonstrate some of the challenges involved in detecting emotions from text. 2010 Conference Paper http://hdl.handle.net/20.500.11937/36906 10.1109/DEST.2010.5610650 IEEE fulltext |
| spellingShingle | Text classification Sentiment analysis Emotion detection Emotion models Binali, Haji Wu, Chen Potdar, Vidyasagar Computational Approaches for Emotion Detection in Text |
| title | Computational Approaches for Emotion Detection in Text |
| title_full | Computational Approaches for Emotion Detection in Text |
| title_fullStr | Computational Approaches for Emotion Detection in Text |
| title_full_unstemmed | Computational Approaches for Emotion Detection in Text |
| title_short | Computational Approaches for Emotion Detection in Text |
| title_sort | computational approaches for emotion detection in text |
| topic | Text classification Sentiment analysis Emotion detection Emotion models |
| url | http://hdl.handle.net/20.500.11937/36906 |