Listener: improving news experience with AI-added context, text-to-speech, and sentiment analysis

• Main Author: Heng, Wei Jie
• Other Authors: Owen Noel Newton Fernando
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; News aggregation; gpt; Text-to-speech
• Online Access: https://hdl.handle.net/10356/175149
• Institution: Nanyang Technological University

In today’s digital landscape, the deluge of news sources presents a significant obstacle for users seeking to access pertinent information efficiently. Listener is an innovative web application poised to revolutionize the news consumption experience by leveraging cutting-edge AI technologies. Listener is meticulously crafted to mitigate the challenges of information overload, offering users a streamlined and personalized approach to news consumption. At the core of Listener’s functionality lies the integration of advanced AI tools, prominently featuring OpenAI’s gpt-3.5-turbo for robust content summarization and contextual enrichment. This distill complex news articles into concise summaries while providing additional context that users may seek to further their understanding. Moreover, OpenAI’s text-to-speech functionality, facilitate seamless auditory consumption of news content. This feature not only caters to users with visual impairments but also enhances multitasking capabilities, allowing individuals to consume news on the go or while engaging in other activities. In conclusion, Listener represents how AI can be used to further enrich the news consumption experience with all these new technologies.