David E. Losada, Universidad de Santiago de Compostela, Spain
The challenge of understanding the flow of sentiments in social media documents
This talk is focused on a key task in the area of Opinion Mining and Sentiment Analysis: polarity classification of social media documents (e.g. blog posts). Estimating polarity is much more demanding than estimating topicality. As a matter of fact, the effectiveness of polarity classification is still modest and does not compare with the effectiveness of standard retrieval tasks. Polarity estimation is severely affected by parts of the text that are off-topic or that simply do not express any opinion. In fact, the key sentiments in a document often appear in specific locations of the text. Furthermore, there are usually conflicting opinions in a given document and this mixed set of opinions harms the performance of automatic methods designed to estimate the overall orientation of the text. In this talk, I will argue that understanding the flow of sentiments in a text is a major challenge for effectively predicting the document's orientation towards a given topic. I will briefly outline some possible avenues to address this challenging issue and review some recent papers that take steps in this direction.
Joemon M. Jose, University of Glasgow, UK
Information Retrieval techniques for social media
Martin Atzmüller, University of Kassel, Germany
Analysis of communities in social media
Social media have already woven themselves into the very fabric of everyday life. There are a variety of applications and associated computational social systems. Furthermore, we observe the emergence into more mobile and ubiquitous applications. Various social applications provide for a broad range of user interaction and communication. In this setting, data mining and analysis plays a central role, e.g., for automatically detecting associations and relationships, and identifying interesting topics. In particular, in this talk I will consider the discovery and analysis of communities, e.g., concerning users and user-generated content. Such communities can be applied, for example, for personalization or generating recommendations. However, while there exists a range of community mining options, a thorough evaluation and assessment typically relies on existing gold-standard data or costly user-studies.
This talk presents approaches for the analysis of communities and descriptive patterns in social media. Methods for mining and assessing communities and descriptive patterns will be introduced. The proposed analysis methodology provides for a cost-efficient approach for identifying descriptive and user-interpretable communities, since the assessment is performed using secondary data that is easy to acquire.
In this talk, I will provide examples for the presented analysis techniques using social data from real-world systems. In particular, I will focus on data from the social bookmarking system BibSonomy (http://www.bibsonomy.org), and from the social conference guidance system Conferator (http://www.conferator.org).