  Being able to predict stock prices might be the unspoken wish of stock
investors. Although stock prices are complicated to predict, there are many
theories about what affects their movements, including interest rates, news and
social media. With the help of Machine Learning, complex patterns in data can
be identified beyond the human intellect. In this thesis, a Machine Learning
model for time series forecasting is created and tested to predict stock
prices. The model is based on a neural network with several layers of LSTM and
fully connected layers. It is trained with historical stock values, technical
indicators and Twitter attribute information retrieved, extracted and
calculated from posts on the social media platform Twitter. These attributes
are sentiment score, favourites, followers, retweets and if an account is
verified. To collect data from Twitter, Twitter's API is used. Sentiment
analysis is conducted with VADER. The results show that by adding more Twitter
attributes, the MSE between the predicted prices and the actual prices improved
by 3%. With technical analysis taken into account, MSE decreases from 0.1617 to
0.1437, which is an improvement of around 11%. The restrictions of this study
include that the selected stock has to be publicly listed on the stock market
and popular on Twitter and among individual investors. Besides, the stock
markets' opening hours differ from Twitter, which constantly available. It may
therefore introduce noises in the model.
