A study on Email Topic Identification using Latent Dirichlet Allocation integrated with Visual Attention

Email is the most standard documentation tool for communication. Although existing studies use the topic model to support users for classifying emails, they disregard that human is not like a machine can focus on all the words in an email to determine the distribution of email topics. The Latent Dirichlet Allocation (LDA) model forms a basis for inferring topics; our work aims to discover how each word's visual attention influences the topic inference and estimates attention to a word according to its location features. By reviewing the visual-spatial research and the state-of-the-art visual attention models, we select the Bayesian Models to estimate attention and proposing a novel model-Attention orientation Latent Dirichlet Allocation model (AttLDA). We proposed the AttLDA to effectively extract the email topics to improve email message management performance, and it can be considered a feature for settling further tasks.