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%.

翻译：这项工作的主要目标是开发基于LSTM的神经网络,以利用Twitter预测股票市场动态。LSTM网络中使用的单词嵌入器是使用斯坦福的GloVe嵌入器启动的,专门对20亿条推文进行了预先培训。为了克服数据集的有限规模,提议了一个增强战略,将每个输入序列分成150个子集。为了实现原有配置的进一步改进,将进行超单数优化。对高参数变化的影响,如辍学率、批量大小和LSTM隐藏状态输出大小进行单独评估。此外,将审查一套详尽的参数组合,以确定最佳的模型配置。验证数据集的最佳性能是通过超参数组合0.4、8 100(辍学)、批量大小和隐藏单位分别实现的。该模型的最终测试精确度为76.14%。