Twittener : an aggregated news platform

• Main Author: Chan, Wei Chang
• Other Authors: Owen Noel Newton Fernando
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer system implementation
• Online Access: http://hdl.handle.net/10356/76919
• Institution: Nanyang Technological University

Trending topics and news can be found from multiple online sources, such as social media and news portals. This gives rise to the issue of content overloading, whereby users must filter through all content before finding those that are of relevance to them. This project aims to solve these issues by creating a web application called Twittener, which utilises various methods to improve users’ experience and reduce the effort needed to filter through contents. Methods include using text-to-speech technology, sentiment analysis and recommender system. Text-to-speech technology is used on tweets and news abstracts so that people can consume information without paying attention to their screens. This could also be useful for populations with visual impairments. Sentiment analysis on Twitter trends provides useful information regarding each trend and a hybrid recommender system is deployed to recommend users news that would likely interest them. This paper seeks to document the development, implementation, design and implications of Twittener. A survey was also conducted to identify the factors that will increase acceptance rate of such a system by the public.