Converting Sentiment Annotated Data to Emotion Annotated Data

Manasi Kulkarni, Pushpak Bhattacharyya

Abstract
Existing supervised solutions for emotion classification demand large amount of emotion annotated data. Such resources may not be available for many languages. However, it is common to have sentiment annotated data available in these languages. The sentiment information (+1 or -1) is useful to segregate between positive emotions or negative emotions. In this paper, we propose an unsupervised approach for emotion recognition by taking advantage of the sentiment information. Given a sentence and its sentiment information, recognize the best possible emotion for it. For every sentence, the semantic relatedness between the words from sentence and a set of emotion-specific words is calculated using cosine similarity. An emotion vector representing the emotion score for each emotion category of Ekman’s model, is created. It is further improved with the dependency relations and the best possible emotion is predicted. The results show the significant improvement in f-score values for text with sentiment information as input over our baseline as text without sentiment information. We report the weighted f-score on three different datasets with the Ekman’s emotion model. This supports that by leveraging the sentiment value, better emotion annotated data can be created.
Anthology ID:
2019.icon-1.20
Volume:
Proceedings of the 16th International Conference on Natural Language Processing
Month:
December
Year:
2019
Address:
International Institute of Information Technology, Hyderabad, India
Editors:
Dipti Misra Sharma, Pushpak Bhattacharya
Venue:
ICON
SIG:
Publisher:
NLP Association of India
Note:
Pages:
170–177
Language:
URL:
https://aclanthology.org/2019.icon-1.20
DOI:
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Cite (ACL):
Manasi Kulkarni and Pushpak Bhattacharyya. 2019. Converting Sentiment Annotated Data to Emotion Annotated Data. In Proceedings of the 16th International Conference on Natural Language Processing, pages 170–177, International Institute of Information Technology, Hyderabad, India. NLP Association of India.
Cite (Informal):
Converting Sentiment Annotated Data to Emotion Annotated Data (Kulkarni & Bhattacharyya, ICON 2019)
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