Emotion detection state of the art
Emotion recognition and analysis has been extensively researched in neuroscience, psychology, cognitive science and computer science. In the absence of verbal and facial cues commonly associated with recognizing emotions, can emotions be detected via intelligently analyzing textual information? Our...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
ACM
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/8777 |
| _version_ | 1848745758167138304 |
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| author | Binali, Haji Potdar, Vidyasagar |
| author2 | Vidyasagar Potdar |
| author_facet | Vidyasagar Potdar Binali, Haji Potdar, Vidyasagar |
| author_sort | Binali, Haji |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Emotion recognition and analysis has been extensively researched in neuroscience, psychology, cognitive science and computer science. In the absence of verbal and facial cues commonly associated with recognizing emotions, can emotions be detected via intelligently analyzing textual information? Our main goal is to answer this question by presenting a state-of the- art overview of studies that have been carried out in the domain of text based emotion detection. To this end, we will explore how emotions are detected from textual sources. We begin with a discussion of currently prevailing emotion theories that provide the basis for text based emotion detection. To achieve an in depth and scientifically structured evaluation, we study the underlying structure of several approaches in the literature and design an evaluation framework. Existing approaches are critically evaluated against the proposed evaluation framework. In particular, the results of various systems are discussed and contrasted. The outcome of such a detailed analysis highlights the current gaps in scientific literature in the field of automated emotion detection. |
| first_indexed | 2025-11-14T06:22:27Z |
| format | Conference Paper |
| id | curtin-20.500.11937-8777 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:22:27Z |
| publishDate | 2012 |
| publisher | ACM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-87772023-02-02T07:57:37Z Emotion detection state of the art Binali, Haji Potdar, Vidyasagar Vidyasagar Potdar Debajyoti Mukhopadhyay automated emotion detection emotion models emotion theories Emotion recognition and analysis has been extensively researched in neuroscience, psychology, cognitive science and computer science. In the absence of verbal and facial cues commonly associated with recognizing emotions, can emotions be detected via intelligently analyzing textual information? Our main goal is to answer this question by presenting a state-of the- art overview of studies that have been carried out in the domain of text based emotion detection. To this end, we will explore how emotions are detected from textual sources. We begin with a discussion of currently prevailing emotion theories that provide the basis for text based emotion detection. To achieve an in depth and scientifically structured evaluation, we study the underlying structure of several approaches in the literature and design an evaluation framework. Existing approaches are critically evaluated against the proposed evaluation framework. In particular, the results of various systems are discussed and contrasted. The outcome of such a detailed analysis highlights the current gaps in scientific literature in the field of automated emotion detection. 2012 Conference Paper http://hdl.handle.net/20.500.11937/8777 10.1145/2381716.2381812 ACM restricted |
| spellingShingle | automated emotion detection emotion models emotion theories Binali, Haji Potdar, Vidyasagar Emotion detection state of the art |
| title | Emotion detection state of the art |
| title_full | Emotion detection state of the art |
| title_fullStr | Emotion detection state of the art |
| title_full_unstemmed | Emotion detection state of the art |
| title_short | Emotion detection state of the art |
| title_sort | emotion detection state of the art |
| topic | automated emotion detection emotion models emotion theories |
| url | http://hdl.handle.net/20.500.11937/8777 |