Semi-automatic generation of multilingual datasets for stance detection in Twitter.
Saved in:
| Title: | Semi-automatic generation of multilingual datasets for stance detection in Twitter. |
|---|---|
| Authors: | Zotova, Elena1 (AUTHOR) ezotova@vicomtech.org, Agerri, Rodrigo1,2 (AUTHOR) rodrigo.agerri@ehu.eus, Rigau, German2 (AUTHOR) german.rigau@ehu.eus |
| Source: | Expert Systems with Applications. May2021, Vol. 170, pN.PAG-N.PAG. 1p. |
| Subjects: | Natural language processing, Skewness (Probability theory), Natural languages, Social interaction, Social media |
| Abstract: | • New method to semi-automatically build labeled stance detection datasets from Twitter. • Translation strategies outperform zero-shot approaches when data is translated to a high-resourced language. • User-based information helps to label individual tweets. • Our method is applicable to quickly and cheaply generate labeled Twitter-based data. Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partially due to the fact that manually annotating a corpus of social media texts is a difficult, slow and costly process. Furthermore, as stance is a highly domain- and topic-specific phenomenon, the need for annotated data is specially demanding. As a result, most of the manually labeled resources are hindered by their relatively small size and skewed class distribution. This paper presents a method to obtain multilingual datasets for stance detection in Twitter. Instead of manually annotating on a per tweet basis, we leverage user-based information to semi-automatically label large amounts of tweets. Empirical monolingual and cross-lingual experimentation and qualitative analysis show that our method helps to overcome the aforementioned difficulties to build large, balanced and multilingual labeled corpora. We believe that our method can be easily adapted to easily generate labeled social media data for other Natural Language Processing tasks and domains. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 148986708 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Semi-automatic generation of multilingual datasets for stance detection in Twitter. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zotova%2C+Elena%22">Zotova, Elena</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ezotova@vicomtech.org</i><br /><searchLink fieldCode="AR" term="%22Agerri%2C+Rodrigo%22">Agerri, Rodrigo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> rodrigo.agerri@ehu.eus</i><br /><searchLink fieldCode="AR" term="%22Rigau%2C+German%22">Rigau, German</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> german.rigau@ehu.eus</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. May2021, Vol. 170, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Skewness+%28Probability+theory%29%22">Skewness (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+languages%22">Natural languages</searchLink><br /><searchLink fieldCode="DE" term="%22Social+interaction%22">Social interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • New method to semi-automatically build labeled stance detection datasets from Twitter. • Translation strategies outperform zero-shot approaches when data is translated to a high-resourced language. • User-based information helps to label individual tweets. • Our method is applicable to quickly and cheaply generate labeled Twitter-based data. Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partially due to the fact that manually annotating a corpus of social media texts is a difficult, slow and costly process. Furthermore, as stance is a highly domain- and topic-specific phenomenon, the need for annotated data is specially demanding. As a result, most of the manually labeled resources are hindered by their relatively small size and skewed class distribution. This paper presents a method to obtain multilingual datasets for stance detection in Twitter. Instead of manually annotating on a per tweet basis, we leverage user-based information to semi-automatically label large amounts of tweets. Empirical monolingual and cross-lingual experimentation and qualitative analysis show that our method helps to overcome the aforementioned difficulties to build large, balanced and multilingual labeled corpora. We believe that our method can be easily adapted to easily generate labeled social media data for other Natural Language Processing tasks and domains. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=148986708 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eswa.2020.114547 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Natural language processing Type: general – SubjectFull: Skewness (Probability theory) Type: general – SubjectFull: Natural languages Type: general – SubjectFull: Social interaction Type: general – SubjectFull: Social media Type: general Titles: – TitleFull: Semi-automatic generation of multilingual datasets for stance detection in Twitter. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zotova, Elena – PersonEntity: Name: NameFull: Agerri, Rodrigo – PersonEntity: Name: NameFull: Rigau, German IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 170 Titles: – TitleFull: Expert Systems with Applications Type: main |
| ResultId | 1 |
