Probabilistic User-Level Opinion Detection on Online Social Networks

The mass popularity of online social networks such as Facebook and Twitter makes them an interesting and important platform for exchange of ideas and opinions. Accurately capturing the opinions of users from their self-generated data is crucial for understanding these opinion flow processes. We propose a supervised model that uses a combination of hashtags and n-grams as features to identify the opinions of Twitter users on a topic, from their publicly available tweets. We use it to detect opinions on two current topics: U.S. Politics and Obamacare. Our approach requires no manual labeling of features, and is able to identify user opinion with a very high accuracy over a randomly chosen set of users tweeting on each topic.