Abstract
We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.- Anthology ID:
- W17-5205
- Volume:
- Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–49
- Language:
- URL:
- https://aclanthology.org/W17-5205
- DOI:
- 10.18653/v1/W17-5205
- Cite (ACL):
- Saif Mohammad and Felipe Bravo-Marquez. 2017. WASSA-2017 Shared Task on Emotion Intensity. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 34–49, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- WASSA-2017 Shared Task on Emotion Intensity (Mohammad & Bravo-Marquez, WASSA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W17-5205.pdf