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The primary objective of this work is to develop a Neural Network based onLSTM to predict stock market movements using tweets. Word embeddings, used inthe LSTM network, are initialised using Stanford's GloVe embeddings, pretrainedspecifically on 2 billion tweets. To overcome the limited size of the dataset,an augmentation strategy is proposed to split each input sequence into 150subsets. To achieve further improvements in the original configuration,hyperparameter optimisation is performed. The effects of variation inhyperparameters such as dropout rate, batch size, and LSTM hidden state outputsize are assessed individually. Furthermore, an exhaustive set of parametercombinations is examined to determine the optimal model configuration. The bestperformance on the validation dataset is achieved by hyperparameter combination0.4,8,100 for the dropout, batch size, and hidden units respectively. The finaltesting accuracy of the model is 76.14%.

Artificial intelligence prediction of stock prices using social media