Emotion mining using Unsupervised Learning
By: Bhat, Amjad Husain
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Contributor(s): Javed Parvez.
Publisher: New Delhi STM Journals 2018Edition: Vol 5 (3), Sep - Dec.Description: 24- 34p.Subject(s): Computer EngineeringOnline resources: Click Here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: Social networks are considered as the most abundant sources of affective information for Sentiment and Emotion Classification. Emotion Classification is the challenging task of classifying emotions into different types. Emotion is a mental state observed by behavioral or developmental changes. Emotions being universal, the automatic exploration of emotion is considered as the difficult task to be performed. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this paper, we present the technique of Semantic relatedness for automatic classification of Emotion in the text using distributional semantic models. Our approach uses Semantic Similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. Our proposed approach achieved the accuracy of 71.795%, which is competitive considering no training or annotation of data is done.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2021-2021407 |
Social networks are considered as the most abundant sources of affective information for Sentiment and Emotion Classification. Emotion Classification is the challenging task of classifying emotions into different types. Emotion is a mental state observed by behavioral or developmental changes. Emotions being universal, the automatic exploration of emotion is considered as the difficult task to be performed. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this paper, we present the technique of Semantic relatedness for automatic classification of Emotion in the text using distributional semantic models. Our approach uses Semantic Similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. Our proposed approach achieved the accuracy of 71.795%, which is competitive considering no training or annotation of data is done.
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