Monitoring social media: Summarization, classification and recommendation

In this dissertation, we continue previous research on understanding social media documents along three lines: summarization, classification and recommendation. Our first line of work is the summarization of social media documents. Considering the task of time-aware tweets summarization, we first focus on the problem of selecting meaningful tweets given a user’s interests and propose a dynamic latent factor model. Thereafter, given a set of opinionated documents, we address the task of summarizing contrastive themes by selecting meaningful sentences to represent contrastive themes in those documents. A viewpoint is a triple consisting of an entity, a topic related to this entity and sentiment towards this topic. In this thesis, we also propose the task of multi-viewpoint summarization of multilingual social text streams, by monitoring viewpoints for a running topic and selecting a small set of informative documents. Our second line of work concerns hierarchical multi-label classification. Hierarchical multi-label classification assigns a document to multiple hierarchical labels. Here, we focus on hierarchical multi-label classification of social text streams, in which we propose a structured learning framework to classify a short text from a social text stream to multiple classes from a predefined hierarchy. Based on a viewpoint extraction model that we propose as part of a multi-viewpoint summarization task, our third line of work applies a latent factor model for predicting item ratings that uses user opinions and social relations to generate explanations.