News analysis through text mining: a case study

摘要

Purpose – This paper seeks to provide a tangible example of the use of text-mining techniques in a real world setting, i.e. using real, as opposed to test, data. Design/methodology/approach – News stories are modeled using the vector space model, with the similarity between documents quantified using the cosine measure. For data analysis, three clustering algorithms are used, and the results from the best-performing algorithm retained. Findings – Agglomerative clustering performed poorly, while direct k-way clustering and k-way clustering through repeated bisections yielded similar results, with the former performing marginally better in terms of external isolation and internal cohesion of the clusters produced. A number of themes that dominated news coverage during the period under consideration were identified, some of which were noticeably only topical during certain parts of the year.