@inproceedings{kumar-etal-2017-iitpb,
title = "{IITPB} at {S}em{E}val-2017 Task 5: Sentiment Prediction in Financial Text",
author = "Kumar, Abhishek and
Sethi, Abhishek and
Akhtar, Md Shad and
Ekbal, Asif and
Biemann, Chris and
Bhattacharyya, Pushpak",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2153",
doi = "10.18653/v1/S17-2153",
pages = "894--898",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text
%A Kumar, Abhishek
%A Sethi, Abhishek
%A Akhtar, Md Shad
%A Ekbal, Asif
%A Biemann, Chris
%A Bhattacharyya, Pushpak
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F kumar-etal-2017-iitpb
%X 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.
%R 10.18653/v1/S17-2153
%U https://aclanthology.org/S17-2153
%U https://doi.org/10.18653/v1/S17-2153
%P 894-898
Markdown (Informal)
[IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text](https://aclanthology.org/S17-2153) (Kumar et al., SemEval 2017)
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.