关键词: LDA模型, 噪声过滤, 遗传因子, 财经文本, 主题建模

Abstract: Traditional financial studies usually focus on structured data and less on unstructured financial text data, and financial text data contains a huge amount of information. To solve these problems, this paper proposes an improved sliding-window, genetic factor and common financial topic LDA（SGC-LDA） financial text subject research method. Text noise filtering is modeled based on general financial topics to reduce the impact of noise data. Based on sliding window technology, financial genetic factors are introduced to ensure the continuity of topics. The SGC-LDA algorithm which can realize the topic model of financial text is completed. The empirical research based on the real financial texts shows that the theme of the financial text is mainly composed of six main parts：investment and financing, current affairs of people’s livelihood, business dynamics, financial market, macro-economy and industrial economy. The financial theme can be described more completely and accurately by combining the financial theme words and financial text to expand the financial theme. At the same time, the model itself shows certain denoising ability, and the comparative analysis with the benchmark model also confirms the superior classification performance and theme continuity of the model proposed in this paper in the aspect of financial topic modeling.

Key words: LDA model, noise filtering, genetic factor, financial text, topic modeling