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.