A sentiment analysis system for social media using machine learning techniques: Social enablement.
In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets ha...
| Publicado en: | Digital Scholarship in the Humanities Vol. 34; no. 3; pp. 569 - 582 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=138342364&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 138342364 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2019 vid: 34 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 138342364 10.1093/llc/fqy037 ppf: 569 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 791KB tig: atl: A sentiment analysis system for social media using machine learning techniques: Social enablement. aug: au: Rani, Sujata Kumar, Parteek affil: TIET, CSED, India TIET, India su: Sentiment analysis Social learning Mass media use Social systems Machine learning Social media sug: subj: Sentiment analysis Social learning Mass media use Social systems Machine learning Social media ab: In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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