Abstract
Previous studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financial domain and stock market. In this paper, we present a new approach to learning stock market lexicon from StockTwits, a popular financial social network for investors to share ideas. It learns word polarity by predicting message sentiment, using a neural net-work. The sentiment-oriented word embeddings are learned from tens of millions of StockTwits posts, and this is the first study presenting sentiment-oriented word embeddings for stock market. The experiments of predicting investor sentiment show that our lexicon outperformed other lexicons built by the state-of-the-art methods, and the sentiment-oriented word vector was much better than the general word embeddings.- Anthology ID:
- K17-1031
- Volume:
- Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 301–310
- Language:
- URL:
- https://aclanthology.org/K17-1031
- DOI:
- 10.18653/v1/K17-1031
- Cite (ACL):
- Quanzhi Li and Sameena Shah. 2017. Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 301–310, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits (Li & Shah, CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/K17-1031.pdf