Using Fine-grained Emotion Computing Model to Analyze the Interactions between Netizens’ Sentiments and Stock Returns

From the perspective of behavioural finance, this paper combines the fine-grained sentiment calculation with the stock market econometric model to explore the interactions between netizens’ sentiments and stock returns, analyze the differences in the influences of various emotions expressed by netizens on the stock market. First, it constructs a sentiment dictionary for the financial field; then, it calculates the emotion values contained in the text corpus, and constructs a textual sentiment classifier based on the recurrent neural network, calculates the emotion value and establishes the daily netizen sentiment index; and finally, it builds an econometric model to study the interactions between the netizen sentiment index and the stock returns. The results show that this model improves the accuracy of sentiment classification, reduces the number of iterations and saves computing resources; and that the netizen sentiment index, especially, “disgust” and “like”, has significant effects on the stock price changes and transaction volumes, while on the other hand, the listed company’s stock returns data has no reverse effect on the netizen sentiment index

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer