topic modelling; text author identification; text mining; author identification; topic modelling; information retrieval
Inches G., Crestani F. (2012), Overview of the International Sexual Predator Identification Competition, in CLEF Online Working Notes/Labs/Workshop
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Inches G (2011), University of lugano at TREC 2011 microblog track, in NIST Special Publication
Inches Giacomo, Carman Mark James (2011), Investigating the Statistical Properties of User-Generated Documents, in Crestani, fabio
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Inches Giacomo, Basso Andrea (2011), On the generation of rich content metadata from social media, in Crestani, fabio
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Inches Giacomo, Crestani Fabio (2011), Online conversation mining for author characterization and topic identification, in Proceedings of the 4th Workshop for Ph.D. students in information & knowledge management
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Keikha M, Mahdabi P, Gerani S, Inches G, Parapary J, Carman M, Crestani F (2010), University of lugano at TREC 2010, in NIST Special Publication
In this project we will use the latest models of statistical content analysis that are proving successful in the areas of text mining and information retrieval for the mining of conversational content (e.g. Twetter, FaceBook, etc.) for topic identi?cation (what is the conversation about?) and author identi?cation (who are the people involved in the conversation?). Thus, the work proposed has four measurable objectives: (1) Develop a proper evaluation framework for mining conversational content; (2) Develop a number of models for topic modelling and authorship pro?ling for conversational content; (3) Develop an integrated model for topic and author identi?cation/profiling of conversational content; (4) Implement and evaluate a demonstration system of the above integrated model in a realistic application scenario. These objectives will be achieved by applying to the mining of conversational content our past experience in text mining, language and topic modelling, and user/author profiling acquired in a number of past and current research projects. In addition, the project will take advantage and strengthen existing collaborations between the applicants and some very strong research groups in language and topic modelling and author identi?cation.