Developing Semantic Annotation Representation of Social Media Sentiments and Metadata as Resource Description Framework: A Study of Indonesian New Capital Related Tweets Written in Bahasa

Abstract

Social Media has become a tool abiding the press in this modern society. Everyone can write their minds and build their mass media to publish opinions. Thus, in this manuscript, we develop a resource description framework scheme (RDFS) to enrich the information and metadata from Indonesian tweets regarding their New Capitol. This work focused on applying a popular method (i.e., the Tweetskb scheme) to construct the RDF of those tweets. We also developed the Schema to fulfill our need to contain all the information to RDF. RDF Triples were generated by connecting several established vocabularies to ensure the connection between its related nodes has meaning. The sentiment polarity (i.e., neutral, positive, and negative sentiment) is used in this manuscript. Thus, our proposal can be used as an initial work to make use of twitter's metadata to predict how reliable a user is, how the community interact with a certain topic, spam detection, clustering, and even implementing machine learning and deep learning sentiment analysis in a manner of knowledge graph.