Abstract
In this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper’s contributions include a method for handling any sized input and a method for breaking down the conversation into separate parts for easier processing. Finally, clustering was shown to improve results and that such a model for handling sentiment in conversations is both fast and accurate.- Anthology ID:
- S19-2052
- 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:
- 302–306
- Language:
- URL:
- https://aclanthology.org/S19-2052
- DOI:
- 10.18653/v1/S19-2052
- Cite (ACL):
- Jacob Anderson. 2019. Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 302–306, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations (Anderson, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S19-2052.pdf
- Data
- EmoContext