Language model based Chinese financial news sentiment classification

Jun Xu, Ruifeng Xu, Xiaolong Wang
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引用次数: 1

Abstract

This paper address the problem of identifying the sentiment polarity in financial news articles about a public company having potential effect on the future price of the company's stock. The problem is challenging due to the lack of reliable labeled training data and effective classification method. A feasible corpus building strategy is proposed and stock reviews are used for training, since the news polarity prediction is similar to the process of stock analyst drawing their conclusion by weighting the major event pros and cons of the company. The reviews can be annotated automatically by the grade given by the analyst. In addition, the consequent experiments also confirm it. Furthermore, we examine the effectiveness of using language modeling approaches to solve the sentiment classification of Chinese financial news articles. Two different approaches based on language model are employed and their comparisons with SVM and Naive Bayes are also performed in our research. The experiment results justify the effectiveness and robustness of the proposed language model approaches, which perform better than the approaches based on traditional machine learning techniques.
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基于语言模型的中文财经新闻情感分类
本文解决了在金融新闻文章中识别对上市公司股票未来价格有潜在影响的情绪极性的问题。由于缺乏可靠的标记训练数据和有效的分类方法,该问题具有挑战性。由于新闻极性预测类似于股票分析师通过加权公司重大事件的利弊得出结论的过程,因此提出了一种可行的语料库构建策略,并使用股票评论进行训练。可以根据分析人员给出的分数自动对审查进行注释。此外,后续的实验也证实了这一点。此外,我们检验了使用语言建模方法解决中国财经新闻文章情感分类的有效性。本文采用了两种基于语言模型的方法,并与支持向量机和朴素贝叶斯进行了比较。实验结果证明了所提出的语言模型方法的有效性和鲁棒性,其性能优于基于传统机器学习技术的方法。
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