Abstract
With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources.- Anthology ID:
- S19-2024
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 159–163
- Language:
- URL:
- https://aclanthology.org/S19-2024
- DOI:
- 10.18653/v1/S19-2024
- Cite (ACL):
- Joseph Cummings and Jason Wilson. 2019. CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 159–163, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation (Cummings & Wilson, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/autopr/S19-2024.pdf
- Data
- EmoContext