Listening to the crowd: automated analysis of events via aggregated twitter sentiment
Yuheng Hu, Fei Wang, Subbarao Kambhampati · 2013
Individuals often express their opinions on social media platforms like Twitter and Facebook during public events such as the U.S. Presidential debate and the Oscar awards ceremony. Gleaning insight-s from these posts is of importance to analyzing the impact of the event. In this work, we consider the problem of identifying the segments and topic-s of an event that garnered praise or criticism, ac-cording to aggregated Twitter responses. We pro-pose a flexible factorization framework, SOCSEN-T, to learn factors about segments, topics, and sen-timents. To regulate the learning process, several constraints based on prior knowledge on sentimen-t lexicon, sentiment orientations (on a few tweets) as well as tweets alignments to the event are en-forced. We implement our approach using simple update rules to get the optimal solution. We eval-uate the proposed method both quantitatively and qualitatively on two large-scale tweet datasets as-sociated with two events from different domains to show that it improves significantly over baseline models. 1