Abstract
Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task.- Anthology ID:
- S17-2146
- 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:
- 857–860
- Language:
- URL:
- https://aclanthology.org/S17-2146
- DOI:
- 10.18653/v1/S17-2146
- Cite (ACL):
- Narges Tabari, Armin Seyeditabari, and Wlodek Zadrozny. 2017. SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 857–860, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets (Tabari et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2146.pdf