Large Scale Aggregated Sentiment Analytics

In the past years we have witnessed Sentiment Analytics becoming increasingly popular topic in Information Retrieval, which has established itself as a promising direction of research. With the rapid growth of the user-generated content represented in blogs, forums, social networks and micro-blogs, it became a useful tool for social studies, market analysis and reputation management, since it made possible capturing sentiments and opinions at a large scale and with the ever-growing precision. Sentiment Analytics came a long way from product review mining to full-fledged multi-dimensional analysis of social sentiment, exposing people attitude towards any topic aggregated along different dimensions, such as time and demographics. The novelty of our work is that it approaches Sentiment Analytics from the perspective of Data Mining, addressing some important problems which fall out of the scope of Opinion Mining. We develop a framework for Large Scale Aggregated Sentiment Analytics, which allows to capture and quantify important changes in aggregated sentiments or in their dynamics, evaluate demographical aspects of these changes, and explain the underlying events and mechanisms which drive them. The first component of our framework is Contradiction Analysis, which studies diverse opinions and their interaction, and allows tracking the quality of aggregated sentiment or detecting interesting sentiment differences. Targeting large scale applications, we develop a sentiment contradiction measure based on the statistical properties of sentiment and allowing efficient computation from aggregated sentiments. Another important component of our framework addresses the problem of monitoring and explaining temporal sentiment variations. Along this direction, we propose novel time series correlation methods tailored specifically for large scale analysis of sentiments aggregated over users demographics. Our methods help to identify interesting correlation patterns between demographic groups and thus better understand the demographical aspect of sentiment dynamics. We bring another interesting dimension to the problem of sentiment evolution by studying the joint dynamics of sentiments and news, uncovering the importance of news events and assessing their impact on sentiments. We propose a novel and universal way of modeling different media and their dynamics, which aims to describe the information propagation in news- and social media. Finally, we propose and evaluate an updateable method of sentiment aggregation and retrieval, which preserves important properties of aggregated sentiments and also supports scalability and performance requirements of our applications.