Abstract
This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.- Anthology ID:
- S17-2140
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 827–831
- Language:
- URL:
- https://aclanthology.org/S17-2140
- DOI:
- 10.18653/v1/S17-2140
- Cite (ACL):
- Zarmeen Nasim. 2017. IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 827–831, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News (Nasim, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S17-2140.pdf