Intelligent Sentiment Analysis On Cellphone Checking By Police Officer

Abstract

Twitter is one of social media used by the public to send and read tweets that have been shared, making it easier to express their opinions. The opinions found on Twitter are perceptions, both positive and negative. The abundance of public opinion can be used as research material to find information. The use of this information requires appropriate analytical techniques so that the gained information may help many parties in making decisions. Sentiment analysis technique is one of them. In this study, sentiment analysis was carried out to find out the public's opinion on the case of civilians' cellphone inspection/checking by a police officer. Naïve Bayes classifier and Support Vector Machine (SVM) classifier as comparison are employed and combined with RapidMiner and Python tools. Experimental results show SVM classifier provides the highest accuracy value of 94% for a 90:10 split ratio dataset with automatic labeling.