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| Ultima descărcare din IBN: 2021-06-11 19:10 |
 SM ISO690:2012BOBICEV, Victoria, SOKOLOVA, Marina. Sentiment analysis of user-generated online content. In: Telecommunications, Electronics and Informatics, Ed. 5, 20-23 mai 2015, Chișinău. Chișinău, Republica Moldova: 2015, Ed. 5, pp. 335-338. ISBN 978-9975-45-377-6. |
| EXPORT metadate: Google Scholar Crossref CERIF DataCite Dublin Core |
| Telecommunications, Electronics and Informatics Ed. 5, 2015 |
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Conferința "Telecommunications, Electronics and Informatics" 5, Chișinău, Moldova, 20-23 mai 2015 | ||||||
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This paper presents several experiments in the domain of automate text sentiment analysis. Comparison between machine learning (ML) and rule-based algorithms demonstrated that well-tuned rule-based methods obtain better results than general ML methods and it is necessary to use various types of features for obtaining satisfactory accuracy using ML algorithms. |
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| Cuvinte-cheie Natural Language Pricessing, Rule – based methods, Semantic Lexicons, text analysis, sentiment analysis, Machine Learning Algorithms |
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Dublin Core Export
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc='http://purl.org/dc/elements/1.1/' xmlns:oai_dc='http://www.openarchives.org/OAI/2.0/oai_dc/' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xsi:schemaLocation='http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd'> <dc:creator>Bobicev, V.</dc:creator> <dc:creator>Sokolova, M.</dc:creator> <dc:date>2015</dc:date> <dc:description xml:lang='en'><p>This paper presents several experiments in the domain of automate text sentiment analysis. Comparison between machine learning (ML) and rule-based algorithms demonstrated that well-tuned rule-based methods obtain better results than general ML methods and it is necessary to use various types of features for obtaining satisfactory accuracy using ML algorithms.</p></dc:description> <dc:source>Telecommunications, Electronics and Informatics (Ed. 5) 335-338</dc:source> <dc:subject>Natural Language Pricessing</dc:subject> <dc:subject>text analysis</dc:subject> <dc:subject>sentiment analysis</dc:subject> <dc:subject>Machine Learning Algorithms</dc:subject> <dc:subject>Rule – based methods</dc:subject> <dc:subject>Semantic Lexicons</dc:subject> <dc:title>Sentiment analysis of user-generated online content</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> </oai_dc:dc>
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