Exchange rate market trend prediction based on sentiment analysis

Exchange rate market is an important part of the global economy to reflect national economic development.With the rise of the Internet, more and more people choose to express their opinions on the Internet. Therefore, we believe that the comments in the online text will also reflect the trend of the exchange rate market. In this paper, a hybrid model of exchange rate market trend prediction based on sentiment analysis is proposed. The sentiment analysis module is constructed by combining Word2vec with Long Short-Term Memoryto extract "emotion" factors in text data and improve the performance of the model.After data cleaning, the model generates word vector and conducts sentiment analysis to get three emotional classifications of "positive", "ordinary" and "negative" by crawling the contents of "exchange rate" entries on "Weibo", and obtains the emotional weight of each day.Then, the emotional weights of about 3 months were randomly inserted into the historical exchange rate data of 3-4 years to conduct the mixed training combining Convolutional Neural Network and LSTM.In this paper, the historical exchange rate data of RMB against US dollar, Japanese yen, Euro and Australian dollar in 3-4 years are used as experimental samples. Experimental results show that compared with some traditional models, the method proposed in this paper is used to conduct comparative analysis by Mean absolute error, Mean squared error, Mean absolute percentage error and R2_Score after inserting small emotional data. The results show that our proposed method outperforms all comparison models in all indicators. Taking the trend prediction of the Australian dollar/RMB exchange rate as an example, our method outperforms the comparison models by an average of about 25.32% in the MAE results, 38.51% in the MSE results, and 2.76% in the MAPE results, R2_Score The average result of Score is 6.16% better than the comparison models, indicating a certain degree of robustness. In order to verify the effectiveness of the emotional weights we extracted in predicting exchange rate trends, we extracted the proposed method from the "emotional analysis" module and conducted a self comparison. The results showed that the results of the module with the addition of "emotional analysis" were significantly better than those without the addition of "emotional analysis" module.