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

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

doi:10.18653/v1/S17-2153

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

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

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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.

Anthology ID:: S17-2153
Volume:: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:: August
Year:: 2017
Address:: Vancouver, Canada
Editors:: Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:: SemEval
SIG:: 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)
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PDF:: https://aclanthology.org/S17-2153.pdf

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@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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%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

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Markdown (Informal)

[IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text](https://aclanthology.org/S17-2153/) (Kumar et al., SemEval 2017)

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