基于深层融合的股票文本主题识别

摘要/Abstract

摘要： 股票市场在资本市场中占据着重要地位,是经济的晴雨表。专家对股票的评论是投资者进行投资决策的重要依据。因此,如何快速有效地捕获众多专家股评的主题信息,成为股票研究领域的热点。然而目前大多数股票文本主题识别算法,其特征选择方法及分类模式多采用单一的标准。一般而言,单一的标准只能从某个侧面反映文本主题的识别效果,无法全面捕获目标的主体特征。事实上,不同的特征选择标准及分类器模型从不同侧面去理解文本,捕获的特征信息具有较强的互补性。为了提高股票文本主题识别的准确性,文章从信息融合的角度对股票文本进行了多层面融合:1)特征选择层,对多种特征选择方法进行加权融合,使其能够全面表征股票文本的特点;2)决策层,基于SVM-score,对多个分类器进行决策层融合,使其能够提高文本识别的准确性。基于实测数据的实验表明:相比单一模式的文本主题识别方法,文章提出的多层融合算法的识别精度明显更高。

关键词: SVM-score, 特征融合, 特征选择, 文本分类, 主题识别

Abstract: The stock market occupies an important position in the capital market and is a barometer of the economy.Experts' comments on stocks are an important basis for investors to make investment decisions.Therefore,how to quickly and effectively capture the subject information of many expert stock reviews has become a hot spot in the field of stock research.However,most stock text topic recognition algorithms currently use a single standard for their feature selection methods and classification models.In general,a single standard can only reflect the recognition of a text topic from one side,and cannot fully capture the subject's main features.In fact,different feature selection criteria and classifier models understand the text from different sides,and the captured feature information has strong complementarity.To this end,in order to improve the accuracy of the theme recognition of stock texts,this paper has a multi-faceted fusion of stock texts from the perspective of information fusion,it includes:1)Feature selection layer,which performs weighted fusion on multiple feature selection methods to enable it to fully characterize stock text features;2)The decision-making layer,based on SVM-score,performs decision-making layer fusion on multiple classifiers,which can improve the accuracy of text recognition.Experiments based on measured data show that the recognition accuracy of the multi-layer fusion algorithm proposed in this paper is significantly improved compared with the single-mode text topic recognition method.

Key words: Feature fusion, Feature selection, Subject recognition, SVM-score, Text categorization

中图分类号:

TP391

张加惠, 陈致远, 赵峰, 安志勇, 谢青松. 基于深层融合的股票文本主题识别[J]. 计算机科学, 2019, 46(11A): 122-126. https://doi.org/

ZHANG Jia-hui, CHEN Zhi-yuan, ZHAO Feng, AN Zhi-yong, XIE Qing-song. Stock Text Theme Recognition Based on Deep Fusion[J]. Computer Science, 2019, 46(11A): 122-126. https://doi.org/

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