Abstract:
The stock market fluctuates a lot on daily basis and these shifts can be difficult to pre dict. Stocks prediction is a major challenge as it depends on many factors. Different
events show that the influence of a user on social media, who tweets about a company,
can impact the stock market movement in terms of high and low prices. In this research,
the main objective is to explore the influence of Twitter users along with their tweet
sentiment values to predict the next day’s stock opening price of Pakistan’s petroleum
companies. Two types of the dataset were used in this study. The one was collected from
Twitter in which tweets were extracted related to Pakistan’s petroleum companies along
with user’s profile-based attributes and tweet-based attributes. The second dataset of
stock historical prices is scraped from Pakistan Stock Exchange (PSX) website. Data
preprocessing is performed on both datasets involving data cleaning, data normaliza tion, and imputation. Valence Aware Dictionary and sEntiment Reasoner (VADER)
was used for calculating the sentiment score of users’ tweets. For influence detection,
this research study introduced a comprehensive and composite approach to identify the
overall influence of Twitter users among other users. The final dataset is then used with
different machine learning algorithms, ensemble methods, and neural network models
for the prediction of the next day’s stock open price. After the selection of hyper pa rameters, the models were trained using a training dataset. Trained models were tested
using a test dataset and the performance of each model is observed by using standard
evaluation indicators such as Root Mean Squared Error (RMSE) and R-Squared (R2).
XGBoost ensemble model achieved the finest prediction results by achieving the lowest
Root Mean Squared error of 0.026 and highest R-squared of 0.98. The final results
demonstrate that the proposed idea of stock prediction with the help of the influence of
Twitter users along with their sentiments could be used to predict the next day’s stock
opening price, which can help investors to make informed decisions.
