A Synchronized Framework for Stock Market Prediction Using OpenAI Sentiment Analysis of Social Media
Authors: Yanjun Li, Anthony Chu


Reviewer paper summaries: This work explores the use of tweet sentiments to assist the classification of stock market price direction in the next window (go up, go down, or remain unchanged).
The stock market price to predict uses three major indices from the New York Stock Exchange, to be stable.
Tweets are retrieved using 4 keywords "stock market, economy, bond and inflation", and the tweet sentiments are predicted using ChatGPT as positive, negative and neutral.
Tweet features can then be created as proportions of positive, negative, and neutral tweets within a window, as well as total tweet counts.
The work uses tweet features of current and previous windows, as well as features for stock index in the previous window, to predict the stock index direction in the current window, using standard sklearn models such as random forest.
Summary of strengths: S1. The idea of using F (tweet occurrence time), E (tweet influential time extension) and O (lag from tweet to market reaction) is interesting and Figs. 1 and 2 are well plotted to illustrate the idea.

S2. A lot of efforts have been devoted to curate dataset and running predictive experiments.
Summary of weaknesses: The novelty is low. It directly uses ChatGPT to classify tweet sentiment, and directly uses the tweet class statistics as features to feed into standard sklearn models (Lines 222-224). Even the trivial RNN solution is treated as future work (Lines 304-307) meaning that the work does not consider any historical window beyond the immediate previous one.
Comments, suggestions and typos: The efforts and workloads in this work is well appreciated but novelty is lacking for a top NLP conference.
Overall assessment scores: 1
Metareview score: 2
Metareview: The paper "A Synchronized Framework for Stock Market Prediction Using OpenAI Sentiment Analysis of Social Media" presents work on creating a data set of tweets related to stock market data, which are analyzed for sentiment in order to align them with stock market data. The final data set is then used for predictions of stock market fluctuations.
Summary of reasons to publish: The data set itself is a valuable contribution, as well as the alignment and the analysis of the tweets and their relation to the stock market.