Twitter Classification of Public Service Complaints

Abstract

This Public service become one of the factors of public satisfaction level to government performance. Complaints figure out publics expectation and needs. Understanding the complaints lead to a possibility to improve public services quality. Twitter is a popular micro-blogging in society. Society can express through their activity, experiences, complaints through the internet easily and real time. In this research the classification society complaints through tweeters into water, electricity, and roads. The twitter classification is built using the K-Nearest Neighbor (KNN) algorithm. The feature selection in this research are using term frequency (TF), document frequency (DF), information gain, and chi square. In this research, combination of features that have been produced from the previous feature selection. The experiments result shows that K-NN is able to classify complaints The accuracy of complaints detection and complaints classification are achieved at 83.75% and 77.08% respectively. For the classification of types of water complaints, the average values generated for each parameter are 87.5% for precision, 87.8% for recall, and 87.49% for F-Mesure testing.