  The primary objective of this work is to develop a Neural Network based on
LSTM to predict stock market movements using tweets. Word embeddings, used in
the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained
specifically on 2 billion tweets. To overcome the limited size of the dataset,
an augmentation strategy is proposed to split each input sequence into 150
subsets. To achieve further improvements in the original configuration,
hyperparameter optimisation is performed. The effects of variation in
hyperparameters such as dropout rate, batch size, and LSTM hidden state output
size are assessed individually. Furthermore, an exhaustive set of parameter
combinations is examined to determine the optimal model configuration. The best
performance on the validation dataset is achieved by hyperparameter combination
0.4,8,100 for the dropout, batch size, and hidden units respectively. The final
testing accuracy of the model is 76.14%.
