Graph Approach Markov Assumptions for Social LDA Inspection

Abstract

Content analysis is applied social science research method is increasingly being supplemented by topic modeling. This approach presents a novel method for automatically detecting and tracking news topics from multimodal TV news data. We propose a Multimodal Topic And-Or Graph (MT-AOG) to jointly represent textual and visual elements of news stories and their latent topic structures. We find news topics through a cluster sampling process which groups stories about closely related events together. Swenson Wang Cuts (SWC), an effective cluster sampling algorithm, Our system demonstrate that incorporating event information in the prediction tasks reduces the root mean square error (RMSE) of prediction by 22% compared to the standard ARIMA model. We present a method for automatically collecting television news and social media content (Twitter) and discovering the hash tags that are relevant for a TV news video. Our algorithms incorporate both the visual and text information within social media and television content and improve performance over single modality methods. This paper present LDA-style topic model that captures not only the low-dimensional structure of data, structure changes over time Unlike other recent work that relies on Markov assumptions of time here each topic is associated with a continuous distribution over timestamps, and for each generated document the mixture distribution over topics is influenced by both word co-occurrences and the document’s timestamp.