Text Mining Based on Tax Comments in Python

The tax gives an important role for the contributions of the economy and development of a country. The improvements to the taxation service system continuously done in order to increase the State Budget. One of consideration to know the performance of taxation particularly in Indonesia is to know the public opinion as for the object service. Text mining can be used to know public opinion about the tax system. The rapid growth of data in social media initiates this research to use the data source as big data analysis. The dataset used is derived from Facebook and Twitter as a source of data in processing tax comments. The results of opinions in the form of public sentiment in part of service, website system, and news can be used as consideration to improve the quality of tax services. In this research, text mining is done through the phases of text processing, feature selection and classification with Support Vector Machine (SVM). To reduce the problem of the number of attributes on the dataset in classifying text, Feature Selection used the Information Gain to select the relevant terms to the tax topic. Testing is used to measure the performance level of SVM with Feature Selection from two data sources. Performance measured using the parameters of precision, recall, and F-measure.