Analysis and visualization of time series data from consumer-generated media and news archives

Internet has become an indispensable part of everyday life with millions of people around the globe using it for a wide range of daily activities such as monitoring stock prices, posting blogs, and browsing online newspapers. Though a vast amount of information can be easily searched and obtained in seconds simply by pressing a click with a fingertip, the overflow of information popping up may not be something really relevant to what we need and therefore, it creates a headache to us when it comes to scanning and extracting relevant and useful information. Finding a wise way of extracting only the useful data for further analysis plays a significant role in promoting the efficient and effective use of the internet. In this paper, we present a system which performs the analysis and visualization of the emerging consumer generated media (CGM) posts and online news archives in a more user-friendly way. In order to overcome the heavy time complexity incurred, we would employ an approach to extract only the useful data from the CGM by means of the Time Series Data Processing technique, namely, the Perceptual Important Point (PIP). By correlating the sorted out time series data with the online texts, further analysis could be done in a more effective and efficient way. With valuable and easy-to-understand information generated by using the Perceptual Important Point (PIP), many businesses could gain the upper hand in today\u27s competitive world market