Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 10, Issue 8, Pages 2199-2207Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-018-0805-x
Keywords
Sentiment analysis; Financial domain; Microblogs; News
Categories
Funding
- Sardinia Regional Government (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014-2020 - Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.)
- Sardinia Regional Government [CUP: F72F16003030002]
- European Union [643808]
Ask authors/readers for more resources
In this paper, a fine-grained supervised approach is proposed to identify bullish and bearish sentiments associated with companies and stocks, by predicting a real-valued score between -1 and +1. We propose a supervised approach learned by using several feature sets, consisting of lexical features, semantic features and a combination of lexical and semantic features. Our study reveals that semantic features, most notably BabelNet synsets and semantic frames, can be successfully applied for Sentiment Analysis within the financial domain to achieve better results. Moreover, a comparative study has been conducted between our supervised approach and unsupervised approaches. The obtained experimental results show how our approach outperforms the others.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available