Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Integrating interactivity into visualising sentiment analysis of blogs
Proceedings of the first international workshop on Intelligent visual interfaces for text analysis
Proceedings of the 8th International Conference on Semantic Systems
Semantic metadata in the news production process: achievements and challenges
Proceeding of the 16th International Academic MindTrek Conference
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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's competitive world market.