SESM: Emotional Social Semantic and Time Series Analysis of Learners’ Comments

Human comments have become an integral part on evaluating the effectiveness of online courses. Most nature language processing studies consider comments as a composition of statistical texts, which distorts its essence in semantic relation and emotional expression from other disciplines' definition. In order to enlarge its denotation and semantics in cross-discipline perspectives, we firstly define online comments as a complex model that could realize feeling communication, express semantic knowledge, prompt social interaction, and fertilize time character. The social-emotional semantic model (SESM) and its complete construction methods are also introduced to extract comment's social and emotional semantic meaning. Utilizing three user-based and topic-based emotional algorithms, the presented model makes it possible to generate topic-based and learner-based time series. Also, this study evaluates the possibility to visualize SESM on 67084 Chinese MOOC comments and 278 time series. The time-varying phenomenon in double time-series may help teachers determine the reason of the emotion change and then decide to conduct course adjustment or personalized instruction. Future learning analysis on comments should consider multiple semantics and emotional time series.