Reddit financial image post sentiment dataset

Area of Science:

• Computational Social Science
• Financial Technology
• Natural Language Processing

Background:

• Financial discussions on social media platforms like Reddit provide insights into market trends and investor sentiment.
• Extracting actionable sentiment data from diverse content formats (text, images) is challenging.

Purpose of the Study:

• To create a novel dataset of sentiment information derived from financial subreddit posts.
• To enable financial forecasting by attributing sentiment to specific financial tickers.

Main Methods:

• Collected data from Reddit and Pushshift APIs, processed via AWS.
• Utilized a fine-tuned MobileNets neural network for image classification into four categories.
• Employed Optical Character Recognition (OCR) and custom methods for text and sentiment extraction from images and text.

Main Results:

• Developed a processed sentiment dataset from financial subreddit image and text posts.
• Classified images into memes, number posts, text posts, and chart posts for targeted analysis.
• Tracked financial tickers to link sentiment to specific financial products.

Conclusions:

• The dataset offers a valuable resource for financial forecasting and analyzing social media sentiment dynamics.
• The methodology allows for the extraction of sentiment signals from complex, multi-modal social media data.
• This work facilitates a deeper understanding of the interplay between social media sentiment and financial markets.