Abstract
In this paper, we present a novel ensemble learning architecture for emotion intensity analysis, particularly a novel framework of ensemble method. The ensemble method has two stages and each stage includes several single machine learning models. In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation. In stage2, we use two regression models including linear regression and XGBoost. The result of stage1 serves as the input of stage2, so the two different type models (linear and non-linear) in stage2 can describe the input in two opposite aspects. We also added a method for analyzing and splitting multi-words hashtags and appending them to the emotion intensity corpus before feeding it to our model. Our model achieves 0.571 Pearson-measure for the average of four emotions.- Anthology ID:
- W17-5234
- 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:
- 243–248
- Language:
- URL:
- https://aclanthology.org/W17-5234
- DOI:
- 10.18653/v1/W17-5234
- Cite (ACL):
- Song Jiang and Xiaotian Han. 2017. DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 243–248, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method (Jiang & Han, WASSA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W17-5234.pdf