Dr. Horacio Saggion (Universitat Pompeu Fabra, Spain)
In the internet era we face the recurring problem of information overload thus creating a major need for computer tools able to constantly distill, interpret, and organize textual information available on-line effectively. Over the last decade a number of technological advances in the Natural Language Processing field have made it possible to analyze, discover, and machine-interpret huge volumes of online textual information. One clear example of these advances in the field is the nowadays ubiquitous presence of automatic translation tools on the Web. However other NLP technologies have still a fundamental role to play in allowing access to textual information.
In this talk I will present an overview of research in the area of Natural Language Processing aiming at facilitating access to textual information online. I will review the role of text summarization, question answering, and information extraction in making textual information more accessible.
I will also discuss the issue of access to textual information for people with learning disabilities and present the ongoing work in this area of the Simplex project which aims at producing a text simplification system to facilitate easy access to textual information.
Prof. Mike Thelwall (University of Wolverhampton, UK)
Sentiment analysis or opinion mining is mainly concerned with the automatic identification of subjectivity and polarity in text. It has been commercially successful because of the potential market research applications for tools that are able to detect customer reactions to products. Recently, new tools have been developed that are able to detect the strength of sentiment in texts with reasonable accuracy across a wide range of social web contexts. The addition of strength information for sentiment promises to allow more fine-grained analyses of bodies of texts. At the same time, there have been a number of large scale non-commercial analyses of texts in the social web that have shed light on wider patterns of sentiment in society or around specific issues. This talk will describe the results from applying a general purpose unsupervised sentiment strength algorithm to two social web contexts: YouTube comments and Twitter posts. The results demonstrate how large scale social trends can be identified relatively easily and how small scale network-based interactions can also be analysed automatically for sentiment.
Mike Thelwall is Professor of Information Science and leader of the Statistical Cybermetrics Research Group at the University of Wolverhampton, UK. He is also Docent at Åbo Akademi University Department of Information Studies, and a research associate at the Oxford Internet Institute. Mike has developed a wide range of tools for gathering and analysing web data, including hyperlink analysis, sentiment analysis and content analysis for Twitter, YouTube, MySpace, blogs and the web in general. His 300+ publications include 156 refereed journal articles, seven book chapters, and two books including Introduction to Webometrics, is an associate editor of the Journal of the American Society for Information Science and Technology and sits on six other editorial boards.