@inproceedings{gaillat-etal-2018-implicit,
title = "Implicit and Explicit Aspect Extraction in Financial Microblogs",
author = "Gaillat, Thomas and
Stearns, Bernardo and
Sridhar, Gopal and
McDermott, Ross and
Zarrouk, Manel and
Davis, Brian",
editor = "Hahn, Udo and
Hoste, V{\'e}ronique and
Tsai, Ming-Feng",
booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3108",
doi = "10.18653/v1/W18-3108",
pages = "55--61",
abstract = "This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gaillat-etal-2018-implicit">
<titleInfo>
<title>Implicit and Explicit Aspect Extraction in Financial Microblogs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Thomas</namePart>
<namePart type="family">Gaillat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bernardo</namePart>
<namePart type="family">Stearns</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gopal</namePart>
<namePart type="family">Sridhar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ross</namePart>
<namePart type="family">McDermott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manel</namePart>
<namePart type="family">Zarrouk</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brian</namePart>
<namePart type="family">Davis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Economics and Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Udo</namePart>
<namePart type="family">Hahn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Véronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ming-Feng</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Melbourne, Australia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.</abstract>
<identifier type="citekey">gaillat-etal-2018-implicit</identifier>
<identifier type="doi">10.18653/v1/W18-3108</identifier>
<location>
<url>https://aclanthology.org/W18-3108</url>
</location>
<part>
<date>2018-07</date>
<extent unit="page">
<start>55</start>
<end>61</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Implicit and Explicit Aspect Extraction in Financial Microblogs
%A Gaillat, Thomas
%A Stearns, Bernardo
%A Sridhar, Gopal
%A McDermott, Ross
%A Zarrouk, Manel
%A Davis, Brian
%Y Hahn, Udo
%Y Hoste, Véronique
%Y Tsai, Ming-Feng
%S Proceedings of the First Workshop on Economics and Natural Language Processing
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F gaillat-etal-2018-implicit
%X This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.
%R 10.18653/v1/W18-3108
%U https://aclanthology.org/W18-3108
%U https://doi.org/10.18653/v1/W18-3108
%P 55-61
Markdown (Informal)
[Implicit and Explicit Aspect Extraction in Financial Microblogs](https://aclanthology.org/W18-3108) (Gaillat et al., ACL 2018)
ACL
- Thomas Gaillat, Bernardo Stearns, Gopal Sridhar, Ross McDermott, Manel Zarrouk, and Brian Davis. 2018. Implicit and Explicit Aspect Extraction in Financial Microblogs. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 55–61, Melbourne, Australia. Association for Computational Linguistics.