Abstract
This paper describes our approach to the sentiment analysis of Twitter textual conversations based on deep learning. We analyze the syntax, abbreviations, and informal-writing of Twitter; and perform perfect data preprocessing on the data to convert them to normative text. We apply a multi-step ensemble strategy to solve the problem of extremely unbalanced data in the training set. This is achieved by taking the GloVe and Elmo word vectors as input into a combination model with four different deep neural networks. The experimental results from the development dataset demonstrate that the proposed model exhibits a strong generalization ability. For evaluation on the best dataset, we integrated the results using the stacking ensemble learning approach and achieved competitive results. According to the final official review, the results of our model ranked 10th out of 165 teams.- Anthology ID:
- S19-2063
- 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:
- 360–364
- Language:
- URL:
- https://aclanthology.org/S19-2063
- DOI:
- 10.18653/v1/S19-2063
- Cite (ACL):
- Dawei Li, Jin Wang, and Xuejie Zhang. 2019. YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 360–364, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation (Li et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S19-2063.pdf
- Data
- EmoContext