This paper applied the Latent Dirichlet Allocation (LDA) methods to select 30 topics for 14817 documents in a school blogging system pressible.org at Teachers College, Columbia University. Sentiment Analysis was conducted to the subsets of documents with different topics by AFINN. Multidimensional (MDS) unfolding method was performed to overlapping of topics, affective and cognitive processes, to identify the associations between emotions and topics. The results indicated that the topics “think and learn”, “music education”, “child development”, “work and think”, “know and think”, “government”, and “build and know” were associated with negative emotions such as anxiety, anger, and sadness. Probabilistic graphical model of 30 LDA topics suggested that there were hidden topics not detected by the word frequencies in LDA algorithms. The directed model of affective and cognitive processes indicated that positive emotions interacted with insights, anxiety led to tentative thoughts and discrepancy. In summary, emotions enhanced thinking processes.
Keywords—blog; online learning community; natural language processing; topic modeling; Latent Dirichlet Allocation; sentiment analysis; unfolding; MDS