BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast window correlations over uncooperative time series
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Local Correlation Tracking in Time Series
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A Novel Visualization Method for Distinction of Web News Sentiment
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Fast approximate correlation for massive time-series data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Journal of the American Society for Information Science and Technology
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
A demographic analysis of online sentiment during hurricane Irene
LSM '12 Proceedings of the Second Workshop on Language in Social Media
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Analyzing sentiments of demographic groups is becoming important for the Social Web, where millions of users provide opinions on a wide variety of content. While several approaches exist for mining sentiments from product reviews or micro-blogs, little attention has been devoted to aggregating and comparing extracted sentiments for different demographic groups over time, such as 'Students in Italy' or 'Teenagers in Europe'. This problem demands efficient and scalable methods for sentiment aggregation and correlation, which account for the evolution of sentiment values, sentiment bias, and other factors associated with the special characteristics of web data. We propose a scalable approach for sentiment indexing and aggregation that works on multiple time granularities and uses incrementally updateable data structures for online operation. Furthermore, we describe efficient methods for computing meaningful sentiment correlations, which exploit pruning based on demographics and use top-k correlations compression techniques. We present an extensive experimental evaluation with both synthetic and real datasets, demonstrating the effectiveness of our pruning techniques and the efficiency of our solution.