Spatial Temporal Topic Embedding: A Semantic Modeling Method for Short Text in Social Network

Abstract

Social network generates massive text data every day, which makes it important to mine its semantics. However, due to the inability to combine global semantics with local semantics, existing semantic modeling methods cannot overcome the sparseness of short texts and the ambiguity of words in different spatial-temporal environments. In this paper, we propose a semantic modeling method for social network short text, named Spatial-temporal topic embedding (STTE), which combines the spatial-temporal global context information and local context information. We first design a topic model that utilizes the text feature, time feature and location feature at the same time to generate accurate spatial-temporal global context information. Then, we employ this global information to predict an explicit topic for each word and regard the combination of each word and its assigned topic as a new pseudo word. After that, we exploit pseudo word sequence as the input of embedding vector model and finally learn the text feature which could reflect the text semantic with social network characteristics. Classification and search experiments in real-world datasets of the social network have verified that the proposed STTE has better semantic modeling ability than other baseline methods.