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| 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 |
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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 userscognition 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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Received: 02 January 2019
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