The proliferation of social media (networks, blogs, e-commerce) has led to an explosion of data and opinion, fueling interest in sentiment and social network analyses, especially as individuals, brands and corporations look to manage their reputations and keep up with business intelligence. In order to effectively distill valuable kernels of information from big data and deduce broader conclusions (sentiment), multiple techniques are necessary to employ. This talk delves into these techniques and how they interrelate to produce accurate and actionable outcomes. You will be taken beyond simple term analysis into the complex world of natural language processing, contextual search, machine learning, text summarization, topic extraction and information structuring (triples extraction). You should be comfortable with (and/or seriously interested in) analytical tables, language parsing, probability formulas and algorithms, term-document matrices, XML, Ontology Web Language, Logic Axioms, and Hadoop/MapReduce paradigms. There will be plenty of examples and case studies presented to show application of techniques and methods, including a sentiment analysis of large textual data sets from Twitter, Amazon.com and TripAdvisor.com.