SAFE: A Sentiment Analysis Framework for E-Learning.
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| Title: | SAFE: A Sentiment Analysis Framework for E-Learning. |
|---|---|
| Authors: | Colace, Francesco1, De Santo, Massimo1, Greco, Luca1 |
| Source: | International Journal of Emerging Technologies in Learning. 2014, Vol. 9 Issue 6, p37-41. 5p. 3 Diagrams, 2 Charts, 1 Graph. |
| Subject Terms: | *Mobile learning, *Social networks, *Information technology, Sentiment analysis, Data mining, Dirichlet forms |
| Abstract: | The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) 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: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 100074145 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SAFE: A Sentiment Analysis Framework for E-Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Colace%2C+Francesco%22">Colace, Francesco</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22De+Santo%2C+Massimo%22">De Santo, Massimo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Greco%2C+Luca%22">Greco, Luca</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2014, Vol. 9 Issue 6, p37-41. 5p. 3 Diagrams, 2 Charts, 1 Graph. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+technology%22">Information technology</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Dirichlet+forms%22">Dirichlet forms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) 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=ehh&AN=100074145 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijet.v9i6.4110 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 37 Subjects: – SubjectFull: Mobile learning Type: general – SubjectFull: Social networks Type: general – SubjectFull: Information technology Type: general – SubjectFull: Sentiment analysis Type: general – SubjectFull: Data mining Type: general – SubjectFull: Dirichlet forms Type: general Titles: – TitleFull: SAFE: A Sentiment Analysis Framework for E-Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Colace, Francesco – PersonEntity: Name: NameFull: De Santo, Massimo – PersonEntity: Name: NameFull: Greco, Luca IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 12 Text: 2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 18630383 Numbering: – Type: volume Value: 9 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Emerging Technologies in Learning Type: main |
| ResultId | 1 |
