Abstract:
The stock market is one of the most significant impact factors for both domestic and
global economies. Traders are always in search of somehow guessing the stock prices
in the near future which can help them not just to increase their profit but to become
top traders in the market. But the question is how to know the price ups and downs
in a certain way? Thus, Stock Market Prediction has become an interesting research
topic nowadays. Market movement is influenced by a number of variables, including
company news and performance, social media, macroeconomic and micro-economic considerations,
and overall industry performance, etc. The stock market is typically not
that simple, linear, and smooth but the market trend can be determined by looking
at the stock’s historical performance or its motivating elements. Due to the perspectives
that investors and other social media users have regarding market movements,
social media plays a key role in market analysis. In this research, we have proposed
a generalized mechanism to predict stock prices using machine learning and sentiment
analysis of Twitter data. We have used VADER which is a particularly attuned sentiment
analysis tool for social media sentiments, and have overcome the challenge of
considering slang words, emojis, emoticons, and punctuation towards sentiment analysis
along with simple text to avoid information loss in the form of removing such real
and visual indicators and to get rid of the data cleaning process. We have scrapped
and used 10 years of Twitter data as well as stock prices of three different companies
i.e. AAPL (Apple), TSLA (Tesla), and INTLC (Intel). Eight different classifiers like KNearest
Neighbours(KNN), Logistic Regression(LogR), Support Vector Machine (SVM),
Decision Tree (DT), Random Forest(RF), Artificial Neural Networks (ANN), Long and
Short Term Memory (LSTM), and 1 Dimensional Convolutional Neural Network(1DCNN)
have been trained and evaluated. We have achieved an accuracy of 98% and DT
is the top-performing model.
