SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations
Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, Maite Martin, L. Alfonso Ureña-López
Abstract
Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using message platforms such as WhatsApp or Telegram. For this reason, it is important to develop systems capable of understanding human emotions in textual conversations. In this paper, we carried out different systems to analyze the emotions of textual dialogue from SemEval-2019 Task 3: EmoContext for English language. Our main contribution is the integration of emotional and sentimental features in the classification using the SVM algorithm.- Anthology ID:
- S19-2053
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 307–311
- Language:
- URL:
- https://aclanthology.org/S19-2053
- DOI:
- 10.18653/v1/S19-2053
- Cite (ACL):
- Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, Maite Martin, and L. Alfonso Ureña-López. 2019. SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 307–311, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations (Plaza-del-Arco et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-2053.pdf
- Data
- EmoContext