In the interdisciplinary field of finance and computing, scholars have proposed a method to calculate investor sentiment and thus predict stock trends from investor comment section data, which has also been transferred to perform analysis of the emotion of the news texts that would affect the investors’ decisions on investment. After the great success of textual sentiment analysis, some scholars found that the pictures’ emotions in the news have similar influences on investor sentiment. However, the problem is that, unlike the texts which can reflect the editor’s attitude directly and effectively, since some editors usually do not deliberately pay attention to the emotions of news pictures, most of them are published as how they look when taken. Few editors intentionally change pictures’ emotions according to their attitudes in post-processing moments. Thus, to apply a more precise stock forecasting model to automatic buying and selling software, it seems significant to figure out whether the news pictures and the texts express similar attitudes and in what kinds of news pictures can accurately convey the attitude of the news editor. We collected articles about finance from four sources and conducted a correlation analysis between textual sentiments and pictures’ emotions to solve these two problems. A modified NLP model provides textual sentiments, and a two-category deep learning model gives pictures’ emotions. On the whole, this work not only helps improve the accuracy of the automatic buying and selling software but also makes automatic control more intelligent.

As chronic diseases such as cardiovascular diseases are prevalent and progressively more common in young people, more and more college students are paying attention to exercising, even though they are busy studying. However, some college students are unmindful of their physique and their bodies’ targeted exercise. The exercise they do is either extensive but not refined or too homogeneous. We conducted a statistical analysis of 18,101 college students’ physical examination results. We found that students who exercise regularly but still did not achieve satisfactory results in one or more physical examination items had often exercised in the two unscientific ways mentioned above. This paper presents an intelligent recommendation system that integrates statistical principles and natural language processing, which improves traditional recommendation systems and could provide suitable and targeted exercise suggestions for college students. The R2 increased by about 27.72%.