Abstract
Political-related news is one of the most popular topics in different media platforms, such as TV channels, Internet news and newspapers in Taiwan. When news are produced through a process of selection and rephrase by reporters and media firms, reporters’ personal political leaning and personal opinions may influence the process, important messages may inevitably loss. In Taiwan, the Parliamentary Library of Legislative Yuan website provides detailed contents about activities happening inside Legislative Yuan, including contents, such as transcripts and video recordings of interpellation, conference speech, interim proposal and legislation proposal. Although there is a complete record of information regarding Legislative Yuan provided online, but the quantity of Legislative documents are far too much for citizens to make sense of. It is imperative that better organized information released to the public would facilitate readers to ease the cognitive loads in understanding what issues have been discussed by legislators and reported by the media. To minimize the gap between legislative documents and the general public, this study proposes a text mining mechanism to automatically cluster legislative and news documents by media frames, and then represents the proportion of each frame on certain sources. This study aims to develop an automatic clustering system to determine media frames with the minimum amount of human interference. Domain expert labeling was not considered to increase classification accuracy, except the stopping words and few minor variables in the feature selection stage requires human adjustment according to input files, and the rest of the system are able to process input documents with zero expert labeling or adjustments. Experimental results evaluated by domain experts shows the system proposed in this study is able to assist and provide political domain experts hard evidences of media framing. People can monitor and discover media framing phenomenon using accurate numbers and data.