A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
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We built a system for the automatic creation of a textbased topic hierarchy, meant to be used in a geographically defined community. This poses two main problems. First, the appearance of both standard language and a community-related dialect, demanding that dialect words should be as much as possible corrected to standard words, and second, the automatic hierarchic clustering of texts by their topic. The problem of correcting dialect words is dealt with by performing a nearest neighbor search over a dynamic set of known words, using a set of transition rules from dialect to standard words, which are learned from a parallel corpus. We solve the clustering problem by implementing a hierarchical co-clustering algorithm that automatically generates a topic hierarchy of the collection and simultaneously groups documents and words into clusters.