Twittener: An Aggregated News Platform

Abstract

The Internet offers an abundance of online sources for trending topics and news. However, this gives rise to the issue of content overload, where users must filter through large amount of content to find those that are of relevance or interest to them. This project aims to solve this issue by creating a web application called Twittener. Twittener aims to improve users' experience and time-efficiency when reading news online. Methods used include text-to-speech technology, sentiment analysis and recommender system. Text-to-speech technology enables users to listen to tweets and news without paying attention to their screens. This could also be useful for populations with visual impairments. Sentiment analysis on Twitter trends provides useful information on general sentiment towards each trend and a hybrid recommender system is deployed to recommend news that would likely be of interest to users. This paper seeks to document the development, implementation, design and implications of Twittener.