摘要:随着经济活动数据的不断丰富，互联网平台上产生了大量的财经文本，其中蕴含了经济领域发展状况的影响因素.如何从这些财经文本中有效地挖掘与经济有关的经济要素，是实现非结构化数据在经济研究中应用的关键.根据人工构建非结构化经济指标的局限性，以及主题模型在非结构化经济指标挖掘中存在的问题，结合已有经济领域分类标准、词语之间的语义关系和词语对主题的代表性，定义了文档的领域隶属度、词语与主题的语义相关度和词语对主题的贡献度，用于分别描述CRF（Chinese restaurant franchise）中餐厅的菜肴风格、顾客之间对菜肴要求的一致程度和顾客对菜肴的专一程度；结合文档领域属性、词语语义和词语在主题中的出现情况，提出了PSP_HDP（combining documents' domain properties，word semantics and words' presences in topics with HDP）主题模型.由于PSP_HDP主题模型改进了文档-主题与主题-词语的分配过程，从而提高了经济主题的区分度和辨识度，可以更有效地挖掘与经济有关的经济主题和经济要素词.实验结果表明：提出的PSP_HDP主题模型不仅在主题多样性、内容困惑度和模型复杂度等评价指标方面的整体性能优于HDP主题模型，而且在非结构化经济指标挖掘和经济要素词抽取方面能够得到区分度更好、辨识度更高的结果.



Abstract:With the increasing enrichment of economic activity data, a large number of financial texts have emerged on Internet, which contains the influence factors of the economic development. How to mine these economic factors from these texts is the key to conduct economic analysis based on unstructured data. Due to the limitation of manual selection of economic indicators, and the inaccuracy of modelling economic indicators in unstructured texts, the CRF (Chinese restaurant franchise) allocation processes in HDP topic model are extended to a more efficient pattern. In order to describe the dish style in a restaurant, the existing economic taxonomies are used to determine the domain membership of a document. The semantic similarity between words is exploited to define the semantic relevance between words and topics, which reflect the similarity of customers' requirements for dishes. For each word, its representativeness of each topic is employed to evaluate its contribution to the topic, which explains the loyalty of a customer to each dish. By combining documents' domain properties, word semantics and words' presence in topics with HDP topic model, a novel model, PSP_HDP topic model, is proposed. As the PSP_HDP topic model improves documents-topics and topics-words allocation processes, it increases the accuracy of identifying economic topics and distinctiveness of the topics, which leads to a more effective mining of economic topics and economic factors. Experimental results show that the proposed model not only achieves a better performance in terms of topic diversity, topic perplexity and topic complexity, but also is effective in finding more cohesive unstructured economic indicators and economic factors.



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