A user-based topic model with topical word embeddings for semantic modelling in social network

Abstract

With the development of the Internet and mobile networks, social networks have gradually become an essential tool and widespread application. Therefore, the research on short text semantic modelling of social networks has attracted widespread attention. However, modelling short texts encounter the semantics sparsity and multiple meanings of a word in social networks. To solve the above problems, we propose a user-based topic model with topical word embeddings semantic modelling method, namely SM-UTM. Firstly, we construct the user topic model to aggregate short text. Secondly, we build word pair in the user topic model to alleviate semantics sparsity in social networks. In addition, we introduce the time information of social networks into the topic model to jointly constrain the generation process of topics, to improve the quality of semantic representation of social network short texts. Finally, we use the topic word embedding learning based on deep learning to train and optimize the word vector according to the learning results of the user topic model, to alleviate the problem of polysemy in social networks. We build multiple groups of quantitative and qualitative experiments based on the crawled real Sina Weibo data. The experimental results show that our SM-UTM is significantly better than the comparison method in the evaluation indicators of topic consistency, purity and entropy.