Sentiment Analysis of Government Social Media towards an Automated Content Analysis Using Semantic Role Labeling

In this paper, we propose to develop an automated content analysis tool to help the Malaysian government’s cyber and legal advisors, as well as the government leaders, to understand public sentiment via their comments which are posted on the official government leaders' or ministerial social media sites (i.e., Twitter, Facebook, etc.). In this study, we explore and apply the Semantic Role Labeling (SRL) techniques that generate new methods to filter and classify the social media content data set, advancing the state of the art in sentiment detection approaches. This proposed automated content analysis tool would be able to provide a platform to measure the impact of public sentiment over the government leader’s postings, and the public’s comments, on their officials’ social media sites. The results and findings from the impact measurement could then be used as a recommendation in the developing or reviewing the national’s cyber communication policy.