Open AccessComparison of Naive Bayes and K-nearest neighbours for online transportation using sentiment analysis in social media
Author(s) -
Aldy Rialdy Atmadja,
Wisnu Uriawan,
Ferdinand Pritisen,
Dian Sa’adillah Maylawati,
Arbain Arbain
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/7/077029
Subject(s) - naive bayes classifier , sentiment analysis , computer science , classifier (uml) , artificial intelligence , k nearest neighbors algorithm , bayes' theorem , social media , machine learning , bayes classifier , statistical classification , data mining , support vector machine , bayesian probability , world wide web
Nowadays, online transportation is one of the transportation that is increasingly preferred by people. It becomes important because people need transportation to be more effective and efficient. However, sentiment analysis is necessary to improve the quality of services on online transportation. Sentiment analysis includes the process of extracting opinions, sentiments, evaluations, and emotions of people about online transportation services on Twitter social media. To get more accuracy in classification, the opinion is taken in large amounts and classify into positive and negative class. There are several steps that use sentiment analysis. Data collection, pre-processing data, POS Tagging, and opinion classification use the Naive Bayes Classifier method, compared to the accuracy of the K-Nearest Neighbours method. The results of the comparison of Naive Bayes Classifier and K-Nearest Neighbours algorithms use 565 data tweets from Twitter, divided 500 trained data, and 65 test data. The result showed that the Naive Bayes Classifier algorithm had achieved the accuracy rate of 66.15%, and K-Nearest Neighbours algorithm produces the accuracy rate of 67.69%. From the results, the K-Nearest Neighbours algorithm perform better accuracy in sentiment classification than Naive Bayes Classifier.