TY - GEN
T1 - A Sentiment Analysis Based Stock Recommendation System
AU - Rao, Jayanth
AU - Ramaraju, Venkat
AU - Smith, James
AU - Bansal, Ajay
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance's conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities which represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics (PEG Ratio, Forward EPS, etc.) to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary "Buy"(1) or "Not Buy"(0) recommendation for each stock. The model achieved an F1 accuracy of 82.5% and for most major stocks, the model's recommendations were aligned with the stock analysts' ratings from the NASDAQ website. The logistic regression model would improve from leveraging a historical compass of data, given the hive-mind behavior that online discussion forums exhibit.
AB - There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance's conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities which represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics (PEG Ratio, Forward EPS, etc.) to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary "Buy"(1) or "Not Buy"(0) recommendation for each stock. The model achieved an F1 accuracy of 82.5% and for most major stocks, the model's recommendations were aligned with the stock analysts' ratings from the NASDAQ website. The logistic regression model would improve from leveraging a historical compass of data, given the hive-mind behavior that online discussion forums exhibit.
KW - Logistic Regression
KW - Sentiment Analysis
KW - Stock Market
KW - VADER
UR - http://www.scopus.com/inward/record.url?scp=85143063554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143063554&partnerID=8YFLogxK
U2 - 10.1109/AIKE55402.2022.00020
DO - 10.1109/AIKE55402.2022.00020
M3 - Conference contribution
AN - SCOPUS:85143063554
T3 - Proceedings - 2022 IEEE 5th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2022
SP - 82
EP - 89
BT - Proceedings - 2022 IEEE 5th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2022
Y2 - 19 September 2022 through 21 September 2022
ER -