Abstract
This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5–subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733.- Anthology ID:
- S17-2148
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 866–871
- Language:
- URL:
- https://aclanthology.org/S17-2148
- DOI:
- 10.18653/v1/S17-2148
- Cite (ACL):
- Leon Rotim, Martin Tutek, and Jan Šnajder. 2017. TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 866–871, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news (Rotim et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/S17-2148.pdf