- Web video categorization is one of the emerging research fields in the computer vision domain due to its massive volume growth in the internet which demands to discover events. Due to insufficient, noisy information and large intra class disparity makes it more daunting task to recognize the events. Most of the recent works focus on constrained (fixed camera, known environment) videos with supervised labelling to categorize the web videos. In this paper, we propose the subject based Part-Of- Speech (POS) Tagging technique with the assist of Named Entity Recognition (NER) and WordNet is applied on YouTube video titles to discover the events based on the subject, not on the objects visualized in the videos. Unsupervised learning method is used on high level features (titles) because of incoming videos are not known and large intra-class variations. For the experiment, we have chosen topics from Google Zeitgeist and downloaded the related videos from YouTube. A novel conclusion is derived from the experimental result that use of low level features will lead to a poor classification in discovering intra class events based on the subject of the videos.