Twittener : aggregated news platform

• Main Author: Tien, Jake Jie Jun
• Other Authors: Owen Noel Newton Fernando
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Computer science and engineering::Computer systems organization::Computer system implementation; Engineering::Computer science and engineering::Information systems::Information storage and retrieval
• Online Access: https://hdl.handle.net/10356/138153
• Institution: Nanyang Technological University

The Internet provides a profusion of online sources for both trending topics and news. With the vast content made available, it might risk readers in information overloading before finding all relevant contents and hence perceive this medium as challenging and overwhelming. Most of the available news sites provide content from official news sites and exclude posts on social media. This paper presents the development of a web application, Twittener, an improved news aggregator that employs several methods to enhance users’ reading experience and time-efficiency when reading news online. Text-to-speech technology, sentiment analysis and recommender system were used to implement the Twittener. Text-to-speech technology allows users to listen to tweets and abstract news without looking at the screen. This could also be useful for populations with visual impairments. Furthermore, sentiment analysis offers valuable information on general sentiment towards a trend and a hybrid recommender system is used to recommend news and topics that would likely be an interest to users. This paper seeks to document the development, implementation, design and implications of Twittener. Additionally, a study was administered to determine the factors that will increase the acceptance rate of such a system by the public.