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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics
Erwin B. Setiawan
Telkom University
Computer Science & IT Electrical & Electronics Engineering

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A Framework for Classifying Indonesian News Curator in Twitter Jaka E. Sembodo; Erwin B. Setiawan; ZK A. Baizal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.4557

Abstract

News curators in twitter are a user, which is interested in following, spreading, giving feedback of recent popular articles. There are two kinds of this user, news curator as human user and news aggregator as bot user. In prior works about news curator, the classification system built using followers, URL, mention and retweet feature. However, there are limited prior works for classifiying Indonesian News Curator in twitter and still hard for labelling data involve just two features: followers and URL. In this paper, we proposed a framework for classifying Indonesian news curator in twitter using Naïve Bayes Classifier (NBC) and added features such as location, bio profile, and common tweet. Another purpose for analysing the influential features of certain class, so its make easier for labelling data of this role in the future. Examination result using percentage split as evaluating system produced 87% accuracy. The most influential features for news curator are followers, bio profile, mention and retweet. For news aggregator class are followers, location, and URL. The rest just common tweet feature for not both class. We implemented Feature Subset Selection (FSS) for increasing system performance and avoiding the over fitting data, it has produced 92.90% accuracy.
Feature Expansion for Sentiment Analysis in Twitter Erwin B. Setiawan; Dwi H Widyantoro; Kridanto Surendro
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.121 KB) | DOI: 10.11591/eecsi.v5.1660

Abstract

The community's need for social media is increasing, since the media can be used to express their opinion, especially the Twitter. Sentiment analysis can be used to understand public opinion a topic where the accuracy can be measured and improved by several methods. In this paper, we introduce a hybrid method that combines: (a) basic features and feature expansion based on Term Frequency-Inverse Document Frequency (TF-IDF) and (b) basic features and feature expansion based on tweet-based features. We train three most common classifiers for this field, i.e., Support Vector Machine (SVM), Logistic Regression (Logit), and Naïve Bayes (NB). From those two feature expansions, we do notice a significant increase in feature expansion with tweet-based features rather than based on TF-IDF, where the highest accuracy of 98.81% is achieved in Logistic Regression Classifier.
Co-Authors Dwi H Widyantoro Jaka E. Sembodo Kridanto Surendro ZK Abdurahman Baizal