U2VDow : Dow 30 Stocks tweets for proposing User2Vec approach

This data set has been collected for "User2Vec: stock market prediction using deep learning with a novel representation of social network users" paper. Stock market prediction is an interesting and challenging problem for investors and financial analysts. Recently, recurrent neural networks like LSTM have shown good performance in the field of stock market prediction. Most current methods use historical market data and in some cases, the dominant direction of users and news for each day. In some cases, the opinions of social network members about the stocks are extracted to improve the prediction accuracy. Usually, the opinions of different users are treated in the same way and are given the same weights in these works. However, it is clear that these opinions have different values based on the accuracy of the prediction of the related user. In this study, the idea is to convert the opinion of each user about each stock into a vector (User2Vec) and then use these vectors to train a Recurrent Neural Network (RNN) and ultimately model the behavior of the users in the market. The proposed user representation is composed of the features extracted from the messages posted in a social network and the market data. Here, we consider the power of the user in predicting the future of the stock based on the social network metrics, e.g. the number of the followers of the user, and the accuracy of its previous predictions. This way, the number of training data is increased and the model is effectively learned. These data are then used to train a stacked bidirectional LSTM network used for aggregating the input data and providing the final prediction. Empirical studies of the proposed model on 30 stocks of 30 Dow Jones clearly shows the superiority of the proposed model over traditional representations. For example, the prediction accuracy is about 93% for the Apple stock which is much higher than the compared models.