SWARM-BASED OPINION MINING MODEL FOR DIGITAL GOVERNANCE

Abstract

Government in a country is the foremost legislative body responsible for taking decisive steps, planning schemes and implementing them with zero margins of error. These schemes and policies directly or indirectly affect the population of the country and direct the rate of social and economic growth. Effective policy framing and implementations have been the primary aim of all governments. But for good governance with long term sustainability taking opinions of the general public becomes indispensable. Twitter is one such open platform for a new type of social interaction where people come forward and express their views not only on products, movies and celebrities but also those critical policies and schemes designed by the government with aim of the overall development. These opinions have a lot more weight and convey a major message to the policymakers if evaluated correctly. This paper elucidates one such framework which mines opinion of general users tweeting on twitter about government policies and classifies them into three different polarities i.e. positive, negative and neutral. Machine Learning and Deep Learning method along with Natural Language Processing techniques has been utilized to extract the sentiments of the tweet and perform analysis on its polarity. The results of this detailed analysis can act as feedback to the governing bodies which can give them a better idea of the demography of the public’s opinion in an effective manner. Thus, this research works presents a technology-based solution for smart governance and interactive policy framing.