Informal Multilingual Multi-domain Sentiment Analysis.
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| Title: | Informal Multilingual Multi-domain Sentiment Analysis. |
|---|---|
| Authors: | Štajner, Tadej1,2 tadej.štajner@ijs.si, Novalija, Inna1, Mladenič, Dunja1,2 |
| Source: | Informatica (03505596). Dec2013, Vol. 37 Issue 4, p373-380. 8p. |
| Subjects: | Multilingual computing, Sentiment analysis, Problem solving, Programming languages, Social media, Feature selection |
| Abstract (English): | This paper addresses the problem of sentiment analysis in an informal setting in multiple domains and in two languages. We explore the influence of using background knowledge in the form of different sentiment lexicons, as well as the influence of various lexical surface features. We evaluate several different feature set combination strategies. We show that the improvement resulting from using a two-layer meta-model over the bag-of-words, sentiment lexicons and surface features is most notable on social media datasets in both English and Spanish. For English, we are also able to demonstrate improvement on the news domain using sentiment lexicons as well as a large improvement on the social media domain. We also demonstrate that domain-specific lexicons bring comparable performance to general-purpose lexicons. [ABSTRACT FROM AUTHOR] |
| Abstract (Slovenian): | Ta članek obravnava problem analize naklonjenosti v neformalnem besedilu v različnih domenah in v dveh različnih jezikih. [ABSTRACT FROM AUTHOR] |
| Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 94334473 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Informal Multilingual Multi-domain Sentiment Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Štajner%2C+Tadej%22">Štajner, Tadej</searchLink><relatesTo>1,2</relatesTo><i> tadej.štajner@ijs.si</i><br /><searchLink fieldCode="AR" term="%22Novalija%2C+Inna%22">Novalija, Inna</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mladenič%2C+Dunja%22">Mladenič, Dunja</searchLink><relatesTo>1,2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Informatica+%2803505596%29%22">Informatica (03505596)</searchLink>. Dec2013, Vol. 37 Issue 4, p373-380. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multilingual+computing%22">Multilingual computing</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+languages%22">Programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: This paper addresses the problem of sentiment analysis in an informal setting in multiple domains and in two languages. We explore the influence of using background knowledge in the form of different sentiment lexicons, as well as the influence of various lexical surface features. We evaluate several different feature set combination strategies. We show that the improvement resulting from using a two-layer meta-model over the bag-of-words, sentiment lexicons and surface features is most notable on social media datasets in both English and Spanish. For English, we are also able to demonstrate improvement on the news domain using sentiment lexicons as well as a large improvement on the social media domain. We also demonstrate that domain-specific lexicons bring comparable performance to general-purpose lexicons. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Slovenian) Group: Ab Data: Ta članek obravnava problem analize naklonjenosti v neformalnem besedilu v različnih domenah in v dveh različnih jezikih. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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=94334473 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 373 Subjects: – SubjectFull: Multilingual computing Type: general – SubjectFull: Sentiment analysis Type: general – SubjectFull: Problem solving Type: general – SubjectFull: Programming languages Type: general – SubjectFull: Social media Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: Informal Multilingual Multi-domain Sentiment Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Štajner, Tadej – PersonEntity: Name: NameFull: Novalija, Inna – PersonEntity: Name: NameFull: Mladenič, Dunja IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 03505596 Numbering: – Type: volume Value: 37 – Type: issue Value: 4 Titles: – TitleFull: Informatica (03505596) Type: main |
| ResultId | 1 |
