基于THUCTC的金融语料情感分析模型优化

doi:10.12052/gdutxb.180016

Abstract

Abstract: Sentiment analysis has attracted interest recently. In financial applications, it can be a reference for investors. However, existing approaches are either so specific as to cause data drift or too general to be precise. Therefore, a general Chinese text classifier for online reviews and news on stocks is optimized. A corpus with 20000 items is first collected. Then, each item is labeled by three persons as ground truth. After that, the THUCTC is optimized, thus optimizing a general Chinese text classifier in three aspects. First, by tokenization, the THUCTC is modified to a 2-gram with a stemming dictionary method and got better results. Second, the best kernel is selected for classifier. The Liblinear kernel is found to be better for people pressed for time. On the other hand, the Libsvm kernel is good at promoting accuracy. Third, a finance-oriented sentiment dictionary is set based on Chi-square and TF-IDF approach. It can be used by on-the-shelf general text classifiers. In this way, the result can be generalized without the loss of preciseness.

Key words: sentiment analysis, text categorization, stock price trend prediction, Chinese word segmentation

CLC Number:

TP181

Rao Dong-ning, Huang Si-hong. Model Optimization of Financial Corpus Sentiment Analysis Based on THUCTC[J].Journal of Guangdong University of Technology, 2018, 35(03): 37-42.

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