Explore the relationship between textual sentiments and pictures' emotions based on unit quantification

Abstract

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.