Extracting insights from social media with large-scale matrix approximations

Abstract

Social media platforms such as blogs, Twitter® accounts, and online discussion sites are large-scale forums where every individual can potentially voice an influential public opinion. According to recent surveys, a massive number of Internet users are turning to such forums to collect recommendations and reviews for products and services, and to shape their individual choices and stances by the commentary of the online community as a whole. The unsupervised extraction of insight from unstructured user-generated web content requires new methodologies that are likely to be rooted in natural language processing and machine-learning techniques. Furthermore, the unprecedented scale of data begging to be analyzed necessitates the implementation of these methodologies on modern distributed computing platforms. In this paper, we describe a flexible new family of low-rank matrix approximation algorithms for modeling topics in a given corpus of documents (e.g., blog posts and tweets). We benchmark distributed optimization algorithms for running these models in a Hadoopi-enabled cluster environment. We describe online learning strategies for tracking the evolution of ongoing topics and rapidly detecting the emergence of new themes in a streaming setting.