Welcome to the InfoSci Platform
Reference Hub4
Emotion Mining Using Semantic Similarity

Emotion Mining Using Semantic Similarity

Rafiya Jan, Afaq Alam Khan
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 22
ISSN: 1947-9093|EISSN: 1947-9107|EISBN13: 9781522544692|DOI: 10.4018/IJSE.2018070101
Cite Article Cite Article

MLA

Jan, Rafiya, and Afaq Alam Khan. "Emotion Mining Using Semantic Similarity." IJSE vol.9, no.2 2018: pp.1-22. http://doi.org/10.4018/IJSE.2018070101

APA

Jan, R. & Khan, A. A. (2018). Emotion Mining Using Semantic Similarity. International Journal of Synthetic Emotions (IJSE), 9(2), 1-22. http://doi.org/10.4018/IJSE.2018070101

Chicago

Jan, Rafiya, and Afaq Alam Khan. "Emotion Mining Using Semantic Similarity," International Journal of Synthetic Emotions (IJSE) 9, no.2: 1-22. http://doi.org/10.4018/IJSE.2018070101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

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. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. 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 article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This 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. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.