Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits

Quanzhi Li, Sameena Shah


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://www.aclweb.org/anthology/K17-1031
DOI:
10.18653/v1/K17-1031
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PDF:
http://aclanthology.lst.uni-saarland.de/K17-1031.pdf