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Opinions from tweets as good indicators of leadership and followership status

Scores of public opinion about two popular world leaders collected from tweets based on the sentiment they exhibited were classified using two Machine learning techniques (Naïve Bayes and Support vector machines), and four features (Words, unigrams, bigrams and negation) for the classification, we f...

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Main Authors: Osanga, I. S., Salim N., N.
Format: Article
Published: Asian Research Publishing Network 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/58694/
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spelling my.utm.586942021-12-07T03:20:49Z http://eprints.utm.my/id/eprint/58694/ Opinions from tweets as good indicators of leadership and followership status Osanga, I. S. Salim N., N. QA75 Electronic computers. Computer science Scores of public opinion about two popular world leaders collected from tweets based on the sentiment they exhibited were classified using two Machine learning techniques (Naïve Bayes and Support vector machines), and four features (Words, unigrams, bigrams and negation) for the classification, we found that the Naïve bayes with unigram features attained a high accuracy of up to 90% therefore indicating that tweets can be used to suggest potential candidates in political election and ways to improve a leaders reputation. © 2006-2015 Asian Research Publishing Network (ARPN. Asian Research Publishing Network 2015 Article PeerReviewed Osanga, I. S. and Salim N., N. (2015) Opinions from tweets as good indicators of leadership and followership status. ARPN Journal Of Engineering And Applied Sciences, 10 (3). pp. 1045-1050. ISSN 1819-6608
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Osanga, I. S.
Salim N., N.
Opinions from tweets as good indicators of leadership and followership status
description Scores of public opinion about two popular world leaders collected from tweets based on the sentiment they exhibited were classified using two Machine learning techniques (Naïve Bayes and Support vector machines), and four features (Words, unigrams, bigrams and negation) for the classification, we found that the Naïve bayes with unigram features attained a high accuracy of up to 90% therefore indicating that tweets can be used to suggest potential candidates in political election and ways to improve a leaders reputation. © 2006-2015 Asian Research Publishing Network (ARPN.
format Article
author Osanga, I. S.
Salim N., N.
author_facet Osanga, I. S.
Salim N., N.
author_sort Osanga, I. S.
title Opinions from tweets as good indicators of leadership and followership status
title_short Opinions from tweets as good indicators of leadership and followership status
title_full Opinions from tweets as good indicators of leadership and followership status
title_fullStr Opinions from tweets as good indicators of leadership and followership status
title_full_unstemmed Opinions from tweets as good indicators of leadership and followership status
title_sort opinions from tweets as good indicators of leadership and followership status
publisher Asian Research Publishing Network
publishDate 2015
url http://eprints.utm.my/id/eprint/58694/
_version_ 1718926034264391680
score 13.18916

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