Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya

Abstract

Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.