@inproceedings{nasim-2017-iba,
title = "{IBA}-Sys at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News",
author = "Nasim, Zarmeen",
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/S17-2140",
doi = "10.18653/v1/S17-2140",
pages = "827--831",
abstract = "This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nasim-2017-iba">
<titleInfo>
<title>IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zarmeen</namePart>
<namePart type="family">Nasim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bethard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marine</namePart>
<namePart type="family">Carpuat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saif</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Mohammad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Cer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.</abstract>
<identifier type="citekey">nasim-2017-iba</identifier>
<identifier type="doi">10.18653/v1/S17-2140</identifier>
<location>
<url>https://aclanthology.org/S17-2140</url>
</location>
<part>
<date>2017-08</date>
<extent unit="page">
<start>827</start>
<end>831</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
%A Nasim, Zarmeen
%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 nasim-2017-iba
%X This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.
%R 10.18653/v1/S17-2140
%U https://aclanthology.org/S17-2140
%U https://doi.org/10.18653/v1/S17-2140
%P 827-831
Markdown (Informal)
[IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News](https://aclanthology.org/S17-2140) (Nasim, SemEval 2017)
ACL