Twitter sentiment classification using Naive Bayes based on trainer perception
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 th...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2017
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-5617 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-56172017-11-21T04:58:27Z Twitter sentiment classification using Naive Bayes based on trainer perception Ibrahim, M.N.M. Yusoff, M.Z.M. 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. 2017-11-21T04:08:35Z 2017-11-21T04:08:35Z 2016 Article 10.1109/IC3e.2015.7403510 en 2015 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2015 8 February 2016, Article number 7403510, Pages 187-189 |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| language |
English |
| description |
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. |
| format |
Article |
| author |
Ibrahim, M.N.M. Yusoff, M.Z.M. |
| spellingShingle |
Ibrahim, M.N.M. Yusoff, M.Z.M. Twitter sentiment classification using Naive Bayes based on trainer perception |
| author_facet |
Ibrahim, M.N.M. Yusoff, M.Z.M. |
| author_sort |
Ibrahim, M.N.M. |
| title |
Twitter sentiment classification using Naive Bayes based on trainer perception |
| title_short |
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_sort |
twitter sentiment classification using naive bayes based on trainer perception |
| publishDate |
2017 |
| _version_ |
1644493735832059904 |
| score |
13.18916 |