Brendan O'Connor

Assistant Professor


Prof. O'Connor develops natural language processing and machine learning methods to analyze social questions in text corpora, such as news or social media. For example: analyzing Twitter to understand how new slang spreads between cities, and how textual sentiment corresponds to public opinion polls. Other applications have looked at censorship in Chinese microblogs and extracting events in international relations from the news. Statistical language patterns can give insight into the underlying social variables (text as measurement); or, they can reveal the socially embedded process of language generation. His interests span a variety of linguistic, computational, and statistical methodologies that are necessary to tackle these questions -- Bayesian inference, optimization, probabilistic graphical models, syntactic parsing, sentiment analysis, crowdsourcing, etc.