Automatic Topic Clustering Using Latent Dirichlet Allocation with Skip-Gram Model on Final Project Abstracts

Topic model has been an elegant method to discover hidden structures in knowledge collections, such as news archives, blogs, web pages, scientific articles, books, images, voices, videos, and social media. The basic model of topic model is Latent Dirichlet Allocation (LDA) and this paper utilizes LDA to automatically cluster topics from final project abstract collection. We compare two methods, that are LDA as a unigram model and LDA with Skip-gram model. Our results are evaluated by an expert on readily available categories. Overall, words from each topic are indeed keywords describing each topic; moreover, the combination of LDA and skip-gram model are capable to capture key phrases from each topic.