Abstract:
The Internet has revolutionized the communication paradigm. This has led towards immense amount ofunstructured data (i.e. textual data), which is a major source to get useful knowledge about people inseveral application domains. TM (Text Mining) extracts high quality information to discover knowledgeby drawing patterns and relationships in textual data. This field has taken great attention of the researchcommunity. As a result, several attempts have been made to propose, introduce and refine techniquesapplied for uncovering knowledge from text data. This study aims at: (1) presenting existing TM techniquesin the scientific literature, (2) reporting challenges/issues and gaps that still need attention, and (3)proposing a framework to bring shape to textual data. A prototype has been developed to demonstrate theeffectiveness and potential worth of proposed approach to display how unstructured data (i.e. news articlesin this study) has been brought to a shape representing interesting knowledge. The proposed frameworkimplements basic NLP (Natural Language Processing) functions in combination of AYLIEN API(Application Programming Interface) functions. The results reveal the fact that how events, celebritiesand popular news-items have been covered in the electronic media, and it also represents subjectivity oftopical news events. The news coverage trends highlight the significance of daily news events, which mayassist in getting insight about the media groups.
