The mass popularity of online social networks, such as Face- book 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 com- bination 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.
['Kasturi Bhattacharjee', 'Linda R. Petzold']
Probabilistic User-Level Opinion Detection on Online Social Networks