Reddit financial image post sentiment dataset.

The dataset presented in this paper consists of sentiment information extracted from image and text data of financial subreddit posts. Members of these subreddits post about their trading behavior, express their opinions, and discuss capital market trends. Their posts contain sentiment information on financial topics as well as signaling information on trading decisions. Frequently, members post screenshots of their portfolios from their mobile broker apps. We collected the posts, processed them to extract sentiment scores using various methods, and anonymized them. The dataset consists therefore not of any content from the posts or information about the author, but the processed sentiment information within the post. Further financial tickers mentioned in the posts are tracked, such that the effect of sentiment in the posts can be attributed to financial products and used in the context of financial forecasting. The posts were collected using the Reddit [2] and Pushshift APIs [3] and processed using an Amazon Web Services architecture. A fine-tuned MobileNets artificial neural network [4] was used to classify images into four distinct categories, which had been determined in a preliminary analysis. The categories included classical memes, number posts (e.g. screenshots of mobile broker portfolios), text posts (e.g. screenshots from twitter) and chart posts (e.g. other financial screenshots, such as charts). The reason for the classification of images into the four categories is that the images are so inherently different, that different extraction methods had to be applied for each category. OCR - methods [5] were used to extract text from images. Custom methods were applied to extract sentiment and other information from the resulting text. The data [1] is available on a 20-minute basis and can be used in many areas, such as financial forecasting and analyzing sentiment dynamics in social media posts.

New Approaches for Monitoring Image Data.

In this paper, we develop new techniques for monitoring image processes under a fairly general setting with spatially correlated pixels in the image. Monitoring and handling the pixels directly is infeasible due to an extremely high image resolution. To overcome this problem, we suggest control charts that are based on regions of interest. The regions of interest cover the original image which leads to a dimension reduction. Nevertheless, the data are still high-dimensional. We consider residual charts based on the generalized likelihood ratio approach. Existing control statistics typically depend on the inverse of the covariance matrix of the process, involving high computing times and frequently generating instable results in a high-dimensional setting. As a solution of this issue, we suggest two further control charts that can be regarded as modifications of the generalized likelihood ratio statistic. Within an extensive simulation study, we compare the newly proposed control charts using the median run length as a performance criterion.