Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information

Lea Canales, Walter Daelemans, Ester Boldrini, Patricio Martínez-Barco


Abstract
Emotion detection has a high potential positive impact on the benefit of business, society, politics or education. Given this, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection: emotional corpora annotation. This will be tackled by proposing a semi-automatic methodology. It consists in two main phases: (1) an automatic process to pre-annotate the unlabelled sentences with a reduced number of emotional categories; and (2) a manual process of refinement where human annotators will determine which is the dominant emotion between the pre-defined set. Our objective in this paper is to show the pre-annotation process, as well as to evaluate the usability of subjective and polarity information in this process. The evaluation performed confirms clearly the benefits of employing the polarity and subjective information on emotion detection and thus endorses the relevance of our approach.
Anthology ID:
R17-1022
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
157–163
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_022
DOI:
10.26615/978-954-452-049-6_022
Bibkey:
Cite (ACL):
Lea Canales, Walter Daelemans, Ester Boldrini, and Patricio Martínez-Barco. 2017. Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 157–163, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information (Canales et al., RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_022