| 基于OCC模型和LSTM模型的财经微博文本情感分类研究 |
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| 引用本文： | 吴鹏,李婷,仝冲,沈思.基于OCC模型和LSTM模型的财经微博文本情感分类研究[J].情报学报,2020,39(1):81-89. |
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| 作者姓名： | 吴鹏 李婷 仝冲 沈思 |
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| 作者单位： | 南京理工大学经济管理学院,南京 210094;江苏省社会公共安全科技协同创新中心,南京 210094;南京理工大学经济管理学院,南京 210094;江苏省社会公共安全科技协同创新中心,南京 210094;南京理工大学经济管理学院,南京 210094;江苏省社会公共安全科技协同创新中心,南京 210094;南京理工大学经济管理学院,南京 210094;江苏省社会公共安全科技协同创新中心,南京 210094 |
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| 基金项目： | 国家自然科学基金项目“突发事件网民负面情感的模型检测研究”（71774084），“社会化影响下个体信息认知处理中的扭曲与偏见机制研究”（71471089）;国家社会科学基金项目“基于社会网络分析的网络舆情主题发现研究”（15BTQ063） |
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| 摘 要： | 为了解决财经微博文本中网民情感状态转移的时序数据分析问题,本文提出一个基于认知情感评价模型(Ortony,Clore&Collins,OCC)和长短期记忆模型(long short term memory,LSTM)的财经微博文本情感分类模型(OCC-LSTM)。基于OCC模型从网民认知角度建立情感规则,对财经微博文本进行情感标注,并作为LSTM模型进行深度学习的训练集;基于LSTM模型,使用深度学习中的TensorFlow框架和Keras模块建立相应的实验模型,进行海量微博数据情感分类,并结合13家上市公司3年的微博文本数据进行实证研究和模型验证对比。实证研究结果发现本文提出的模型取得了89.45%的准确率,高于采用传统的机器学习方式的支持向量机方法 (support vector machine,SVM)和基于深度学习的半监督RAE方法 (semi-supervised recursive auto encoder)。
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| 关 键 词： | 长短期记忆模型 OCC模型 财经微博 情感分类 |
Sentiment Classification of Financial Microblog Text Based on the Model of OCC and LSTM |
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| Wu Peng,Li Ting,Tong Chong,and Shen Si.Sentiment Classification of Financial Microblog Text Based on the Model of OCC and LSTM[J].Journal of the China Society for Scientific andTechnical Information,2020,39(1):81-89. |
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| Authors: | Wu Peng Li Ting Tong Chong and Shen Si |
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| Institution: | (School of Economics and Management,Nanjing University of Science&Technology,Nanjing 210094;Jiangsu Collaborative Innovation Center of Social Safety Science and Technology,Nanjing 210094) |
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| Abstract: | To analyze the time series data of sentimental status transformation of online users in financial microblog text,this paper proposed a model of sentiment classification of financial microblog text based on the Long Short Term Memory model combined with the OCC model. The rules of sentiment were proposed from the view of online users cognition based on the OCC model, and these rules can be taken as training sets for the emotion annotation of the financial microblog texts in the process of deep learning based on the model of LSTM. The sentiment classification task was fulfilled by the Keras module of the TensorFlow framework based on the LSTM model. An experiment was carried out to attest to the utility of the proposed model using financial microblog texts from thirteen listed companies in the last three years. The findings showed that the proposed model achieved 89.45% accuracy, and the accuracy of the proposed model is better than that of the SVM method and the semi supervised RAE method. |
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| Keywords: | Long Short Term Memory model OCC model financial microblog sentiment classification |
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