Long text to image converter for financial reports

In this study, we proposed a novel article analysis method. This method converts the article classification problem into an image classification problem by projecting texts into images and then applying CNN models for classification. We called the method the long text to image converter (LTIC). The features are extracted automatically from the generated images, hence there is no need of any explicit step of embedding the words or characters into numeric vector representations. This method saves the time to experiment pre-process. This study uses the financial domain as an example. In companies' financial reports, there will be a chapter that describes the company's financial trends. The content has many financial terms used to infer the company's current and future's financial position. The LTIC achieved excellent convolution matrix and test data accuracy. The results indicated an 80% accuracy rate. The proposed LTIC produced excellent results during practical application. The LTIC achieved excellent performance in classifying corporate financial reports under review. The return on simulated investment is 46%. In addition to tangible returns, the LTIC method reduced the time required for article analysis and is able to provide article classification references in a short period to facilitate the decisions of the researcher.