Twittener : an aggregated news listener

• Main Author: Loo, Hong Chai
• Other Authors: Owen Noel Newton Fernando
• Format: Final Year Project
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer system implementation
• Online Access: http://hdl.handle.net/10356/73905
• Institution: Nanyang Technological University

Despite the popularity, Twitter is less accessible when the user is driving, exercising, or multitasking which requires the user to be more attentive. To address this limitation, Twittener was initially introduced to facilitate Twitter users to listen to interesting tweets instead of the conventional way of reading them. After an initial preliminary study, Twittener has enhanced by aggregating several news channels and google trends providing latest information to the user. It uses Natural Language Processing to process the crawled tweets to generate sentiment value of each tweet. Text-to-speech technology which convert the categorized tweets into audio form are used to enhance the usability of the system. With the massive information provided by Twitter and news sources, Twittener was built with the goal of helping the user filter and break down information automatically. This is to enhance the efficiency of staying up to date with only the relevant information.