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| A Method for Constructing Topic Map in Professional Social Media：A Case Study of Automobile Forum |
| Lin Jie, Miao Runsheng |
| School of Economics and Management, Tongji University, Shanghai 200092 |
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Abstract The content of topic maps in professional social media includes topics in forums and the relationship among these topics. Topic maps are important in different applications such as determining the direction of professional product innovation and building professional knowledge indices. Based on deep learning and text mining technology, this paper proposes a method for constructing topic maps in professional social media. First, the Skip-Gram model is trained using professional social media text. The hidden layer weight of the model is regarded as the semantic similarities of words, while prediction in the model is regarded as the contextual relevance between words. Second, the existing seed ontology vocabulary is expanded based on semantic similarity and contextual relevance; the aim of this step is to provide high-quality domain vocabulary for the next step of topic extraction. Topics are then extracted by the ontology-based latent Dirichlet allocation (LDA) topic model. Finally, the weight of relevancy between words is defined using semantic similarity and context relevance. The relevancy and hierarchical structure between topics or sub-topics are obtained through graph modeling and spectral clustering. In this paper, the car forum corpus is used to develop a topic map. The results show that the proposed method improved keyword purity by 20.2% compared to the LDA model, and could display the relationships among these topics both clearly and reasonably.
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Received: 25 March 2019
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