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\begin{document}

\title{Stock market prediction based on machine learning and social sentiment
analysis}



\author[1]{Noor Zaman }%
\author[2]{Mustansar Ali Ghazanfar }%
\author[2]{Madiha Anwar }%
\author[2]{Sin Wee Lee }%
\author[2]{Nadeem Qazi }%
\author[2]{Amin Karimi }%
\author[2]{Ali Javed }%
\affil[1]{Taylors University Malaysia}%
\affil[2]{Affiliation not available}%


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  \date{May 25, 2024}


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\begin{abstract}
Precise stock market prediction is crucial for investors, but the
volatility of the stock market is influenced by multiple factors such as
public sentiments, business news, and related product volatility. While
several algorithms have been proposed to predict the stock exchange
index based on historical data, they are not ideal as external factors
play a critical role in market volatility. To address this issue, we
proposed a machine learning model that incorporates historical data with
external factors such as social media sentiments, oil and gold trends,
and financial news data to enhance prediction accuracy. Our study used
HPQ, IBM, ORCL, and MSFT stock market datasets to validate the
effectiveness of the proposed model, including an analysis of the impact
of Covid19 on companies. Our experimental results showed the highest
accuracy of 87.2\% using oil and sentiment datasets. Additionally, we
identified that social media significantly affects IBM stocks, and the
GBM (Gradient Boosting Classifier) classifier produced consistent
results.%
\end{abstract}%



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