Citizen Opinion Mining Initiatives in Government Decision Making by Categorizing Citizen Sentiments

Abstract

 Abstract

Citizen opinion mining (C.O.M.) is a type of natural language processing (NLP) for finding the sentiments of citizen on various targeted government policies. Opinion mining techniques can be used for measuring influence facts like citizen behavior, desire, needs that help to improve government services. It includes building a system to gather and examine opinions for specific policy, made in weblog posts, comments, stories or tweets. Proposed approach gives brief description how citizen’s opinions play important role to make strong decision for any government policy. In our proposed approach, we categorized all the citizen opinion in different categories such as based on their profession (student, professor, common man, businessman) etc. Because if government wants to make decision for specific group of citizen then that group of citizens opinion are most important. The aim of our proposed approach is to make strong decision for citizens.

Keywords: Decision making, data mining, SVM algorithm with RBF kernal function

Cite this Article

Shikha Parashar, R.K. Gupta. Citizen Opinion Mining Initiatives in Government Decision Making By Categorizing Citizen Sentiments. Current Trends in Information Technology. 2017; 7(2): 1–10p.