关键词: 多源主题融合模型, 潜在迪利克雷分布（LDA）, [K]-means, 相似度

Abstract:

Topic detection is the indispensable work of Internet public opinion analysis tasks, a large number of different types of texts have different characteristics but contain the same topic in terms of topic detection, hot topics and so on. The effective utilization of the characteristics of different sources has important scientific and practical significance. Most topic models detect documents from a single source, however, media messages are disseminated from a variety of platforms which have their own attributes with different message length, which makes it difficult to monitor public opinion uniformly. For this purpose, the Multi-source Topic Fusion Model（MTFM） based on co-occurrence relationship is proposed, which incorporates co-occurrence（the same content appears in different places） into multi-source topic fusion model to achieve accurate topic extraction from multiple sources. The experimental results show that, compared with the classical models currently used for topic detection of multiple sources, MTFM can provide an alternative method for topic mining.

Key words: multi-source topic fusion model, Latent Dirichlet Allocation（LDA）, [K]-means, acquaintance degree