IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text

Abhishek Kumar, Abhishek Sethi, Md Shad Akhtar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya


Abstract
This paper reports team IITPB’s participation in the SemEval 2017 Task 5 on ‘Fine-grained sentiment analysis on financial microblogs and news’. We developed 2 systems for the two tracks. One system was based on an ensemble of Support Vector Classifier and Logistic Regression. This system relied on Distributional Thesaurus (DT), word embeddings and lexicon features to predict a floating sentiment value between -1 and +1. The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features. The system was ranked 5th in track 1 and 8th in track 2.
Anthology ID:
S17-2153
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
894–898
Language:
URL:
https://aclanthology.org/S17-2153
DOI:
10.18653/v1/S17-2153
Bibkey:
Cite (ACL):
Abhishek Kumar, Abhishek Sethi, Md Shad Akhtar, Asif Ekbal, Chris Biemann, and Pushpak Bhattacharyya. 2017. IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 894–898, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text (Kumar et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2153.pdf