| dc.creator |
Ramadhani, Rif’at Ahdi |
|
| dc.creator |
Indriani, Fatma |
|
| dc.creator |
Nugrahadi, Dodon T |
|
| dc.date.accessioned |
2020-06-15T03:57:16Z |
|
| dc.date.available |
2020-06-15T03:57:16Z |
|
| dc.identifier |
http://eprints.ulm.ac.id/1401/1/v2_3_1_ICACSIS_2016.pdf |
|
| dc.identifier |
Ramadhani, Rif’at Ahdi and Indriani, Fatma and Nugrahadi, Dodon T Comparison of Naive Bayes Smoothing Methods for Twitter Sentiment Analysis. Comparison of Naive Bayes Smoothing Methods for Twitter Sentiment Analysis. |
|
| dc.identifier.uri |
https://repo-dosen.ulm.ac.id//handle/123456789/9921 |
|
| dc.description |
Abstract— In sentiment analysis, the absence of sample
features in the training data will lead to misclassification.
Smoothing is used to overcome this problem. Previous studies
show that there are differences in performance obtained by the
various smoothing techniques against various types of data. In
this paper, we compare the performance of Naive Bayes
smoothing methods in improving the performance of sentiment
analysis of tweets. The results indicated that Laplace smoothing
is superior to Dirichlet smoothing and Absolute Discounting with
the micro-average value of F1-Score 0.7234 and macro-average
F1-Score 0.7182.
Keywords—Sentiment Analysis, Data Mining, Naive Bayes,
Smoothing, Laplace, Dirichlet, Absolute Discounting |
|
| dc.format |
text |
|
| dc.relation |
http://icacsis.cs.ui.ac.id/ |
|
| dc.relation |
http://eprints.ulm.ac.id/1401/ |
|
| dc.subject |
QA75 Electronic computers. Computer science |
|
| dc.subject |
QA76 Computer software |
|
| dc.title |
Comparison of Naive Bayes Smoothing Methods for
Twitter Sentiment Analysis |
|
| dc.type |
Article |
|
| dc.type |
PeerReviewed |
|