A study on Email Topic Identification using Latent Dirichlet Allocation integrated with Visual Attention
Tzu-Hang Chiang, Yung-Yu Lin, Yukari Nagai, Hua-Ko Chiang · 2021
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.