Sentiment Classification of Financial Microblog Text Based on the Model of OCC and LSTM
Wu Peng1,2, Li Ting1,2, Tong Chong1,2, Shen Si1,2
1.School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094 2.Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094
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）。
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.
Wu Peng, Li Ting, Tong Chong, Shen Si. Sentiment Classification of Financial Microblog Text Based on the Model of OCC and LSTM. 情报学报, 2020, 39(1): 81-89.
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