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|Title:||Web application for analysis of world news on Reddit and news publications||Authors:||Toh, Yue-Sheng||Keywords:||Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2020||Publisher:||Nanyang Technological University||Project:||SCSE19-0198||Abstract:||In the current day and age of modernised society, technology has become an integral part of life. News and social media consumption form a large part of the technology revolution. However, digital news from news publications may be biased and inaccurate due to censorships or other agendas. To overcome this issue, a combination of social media responses and digital news sources can be utilised for analysis. Related works had mainly focussed on social media responses, but not on digital news sources. Existing analysis methods including topic, time, keyword, summary, sentiment and comparison analysis as well as frameworks were researched and improved upon to provide the ideal effectiveness and efficiency for this project. Using a combination of existing web design and analysis tools, the project aims to build a web application which collects and analyses responses from r/worldnews and news articles from BBC and Reuters News. These analytical information will be displayed in a simple web interface to aid users and researchers in understanding the viewpoints of the community as well as identify news that are not reported widely. Data collection was done hourly on all articles from the world news homepage of BBC and Reuters News, as well as the top 30 submissions on r/worldnews, including the top 50 comments of each submission. The implementation of the project was done using Python libraries and frameworks. Future improvements to the project could include an increase in accuracy through advancements in analysis techniques and exploration of different analysis types. Also, improvements to the processing capability of the server and efficiency in database queries can increase the scope of data collection.||URI:||https://hdl.handle.net/10356/139229||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
Updated on Jun 27, 2022
Updated on Jun 27, 2022
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