Twitter sentiment classification using Naive Bayes based on trainer perception

Mohd Naim Mohd Ibrahim, Mohd Zaliman Mohd Yusoff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages187-189
Number of pages3
ISBN (Electronic)9781467394376
DOIs
Publication statusPublished - 08 Feb 2016
EventIEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015 - Melaka, Malaysia
Duration: 24 Aug 201526 Aug 2015

Publication series

Name2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015

Other

OtherIEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015
CountryMalaysia
CityMelaka
Period24/08/1526/08/15

Fingerprint

twitter
Malaysia

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Ibrahim, M. N. M., & Mohd Yusoff, M. Z. (2016). Twitter sentiment classification using Naive Bayes based on trainer perception. In 2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015 (pp. 187-189). [7403510] (2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC3e.2015.7403510
Ibrahim, Mohd Naim Mohd ; Mohd Yusoff, Mohd Zaliman. / Twitter sentiment classification using Naive Bayes based on trainer perception. 2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 187-189 (2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015).
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title = "Twitter sentiment classification using Naive Bayes based on trainer perception",
abstract = "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.",
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Ibrahim, MNM & Mohd Yusoff, MZ 2016, Twitter sentiment classification using Naive Bayes based on trainer perception. in 2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015., 7403510, 2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015, Institute of Electrical and Electronics Engineers Inc., pp. 187-189, IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015, Melaka, Malaysia, 24/08/15. https://doi.org/10.1109/IC3e.2015.7403510

Twitter sentiment classification using Naive Bayes based on trainer perception. / Ibrahim, Mohd Naim Mohd; Mohd Yusoff, Mohd Zaliman.

2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 187-189 7403510 (2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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Ibrahim MNM, Mohd Yusoff MZ. Twitter sentiment classification using Naive Bayes based on trainer perception. In 2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 187-189. 7403510. (2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015). https://doi.org/10.1109/IC3e.2015.7403510