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Reddit financial image post sentiment dataset.

Alexander Fottner1, Yarema Okhrin1, Jonathan Pfahler1

  • 1Department of Statistics, Faculty of Business and Economics, University of Augsburg, Universitaetsstr. 16, 86159 Augsburg, Germany.

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|December 19, 2022
PubMed
Summary
This summary is machine-generated.

This study extracts sentiment from financial subreddit posts, creating a dataset for financial forecasting. The processed data captures market trends and trading decisions from social media discussions.

Keywords:
Image sentimentMeme stocksSentiment analysisSocial media

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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.