Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate that the proposed method can enhance the performance of predicting stock market trend by obtaining effective information from stock news.

Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and are not able to generate finer characterizations of sentiments as well as emotions involved. This paper describes a new opinion analysis scheme, i.e., a multi-level fine-scaled sentiment sensing with ambivalence handling. The ambivalence handler is presented in detail along with the strength-level tune parameters for analyzing the strength and the fine-scale of both positive or negative sentiments. It is capable of drilling deeper into text in order to reveal multi-level fine-scaled sentiments as well as different types of emotions.

A shape smoothing algorithm is presented which uses properties of the medial axis to define segments of the shape border and their prominence relative to the local radius of the shape. Prominence values are used to classify axes as either major or minor, and minor axes are then deleted. An augmented medial axis transform is also defined using an approximate Euclidean distance transform, and medial axis interpolation and linking.