Twitter sentiment classification using Naive Bayes based on trainer perception
E-learning; Social networking (online); Supervised learning; Malaysia; Naive bayes; Sentiment classification; Three categories; Classifiers
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Institute of Electrical and Electronics Engineers Inc.
2023
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| author | Ibrahim M.N.M. Yusoff M.Z.M. |
| author2 | 56258624800 |
| author_facet | 56258624800 Ibrahim M.N.M. Yusoff M.Z.M. |
| author_sort | Ibrahim M.N.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | E-learning; Social networking (online); Supervised learning; Malaysia; Naive bayes; Sentiment classification; Three categories; Classifiers |
| format | Conference Paper |
| id | my.uniten.dspace-22845 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-228452023-05-29T14:12:40Z Twitter sentiment classification using Naive Bayes based on trainer perception Ibrahim M.N.M. Yusoff M.Z.M. 56258624800 22636590200 E-learning; Social networking (online); Supervised learning; Malaysia; Naive bayes; Sentiment classification; Three categories; Classifiers This paper presents strategy to classify tweets sentiment using Naive Bayes techniques based on trainers' perception into three categories; positive, negative or neutral. 50 tweets of 'Malaysia' and 'Maybank' keywords were selected from Twitter for perception training. In this study, there were 27 trainers participated. Each trainer was asked to classify the sentiment of 25 tweets of each keyword. Results from the classification training was then be used as the input for Naive Bayes training for the remaining 25 tweets. The trainers were then asked to validate the results of sentiment classification by the Naive Bayes technique. The accuracy of this study is 90% � 14% measured by total number of correct per total classified tweets. � 2015 IEEE. Final 2023-05-29T06:12:40Z 2023-05-29T06:12:40Z 2016 Conference Paper 10.1109/IC3e.2015.7403510 2-s2.0-84963830519 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963830519&doi=10.1109%2fIC3e.2015.7403510&partnerID=40&md5=80401f05ac52e14f06aa9ceffcebd610 https://irepository.uniten.edu.my/handle/123456789/22845 7403510 187 189 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Ibrahim M.N.M. Yusoff M.Z.M. Twitter sentiment classification using Naive Bayes based on trainer perception |
| title | Twitter sentiment classification using Naive Bayes based on trainer perception |
| title_full | Twitter sentiment classification using Naive Bayes based on trainer perception |
| title_fullStr | Twitter sentiment classification using Naive Bayes based on trainer perception |
| title_full_unstemmed | Twitter sentiment classification using Naive Bayes based on trainer perception |
| title_short | Twitter sentiment classification using Naive Bayes based on trainer perception |
| title_sort | twitter sentiment classification using naive bayes based on trainer perception |
| url_provider | http://dspace.uniten.edu.my/ |