On Opinion Characterization in Social Sensing: A Multi-view Subspace Learning Approach
Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang · 2018
Social sensing has emerged as a new application paradigm in networked sensing where data is collected from humans or devices on their behalf. This paper focuses on the opinion characterization problem in social sensing where the goal is to accurately characterize opinion attributes of the participants (e.g., analyze the sentiments, understand the opinion bias) from their sensor measurements. Several important challenges exist in solving the opinion characterization problem. First, human sensors often generate unstructured data (e.g., text, image, video) in which the opinion attributes are deeply embedded. Second, human sensors naturally generate measurements in different data modalities which encode the opinion attributes differently. Third, the possible imbalance between different data modalities may lead to potential bias in the opinion characterization results. To address the above challenges, this paper develops a Multi-View Opinion Characterization (MVOC) scheme to accurately characterize opinion attributes using a multi-view subspace learning approach. We evaluate the MVOC scheme through the real-world social sensing task of classifying the sentiments of reports from Twitter users. The evaluation results show that our scheme significantly outperforms the state-of-the-art baselines in solving the opinion characterization problem.