# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 2 Literature Review
### NN Techniques in Stock-Price Prediction
This technique was introduced by [PERSON] [139] in 2015 and has since been utilized in numerous studies. For example, [PERSON] et al. [19] introduced an LSTM-based model that incorporated sentiment analysis and employed empirical modal decomposition to break down stock-price sequences. The authors' approach enhanced prediction accuracy by leveraging LSTM's capacity to analyze relationships among time-series data through its memory function. Furthermore, [PERSON] et al. [21] introduced the CNN-BiLSTM-AM method, which combined convolutional neural networks, bidirectional long short-term memory, and an attention mechanism. The authors' model aimed to predict the following-day stock closing prices by extracting features using CNN, using BiLSTM for prediction, and employing an attention mechanism to capture feature influences at different times. A comparative analysis against seven other methods for predicting stock closing prices on the Shanghai Composite Index revealed the superior performance of the CNN-BiLSTM-AM method in terms of MAE and RMSE. [PERSON] et al. [20] employed artificial neural network and random-forest techniques to predict next-day closing prices for stocks across various sectors. By utilizing financial data, such as open, high, low, and closed prices, they created new variables as inputs in the model. The evaluation based on standard indicators RMSE and MAPE highlighted the efficiency of their models for predicting stock closing prices. [PERSON] et al. [60] explored LSTM for stock-price prediction by introducing the S_I_LSTM method in incorporating multiple data sources and investor sentiment. The authors' approach leveraged sentiment analysis based on convolutional NNs for calculating investors' sentiment index and combined this with technical indicators and historical transaction data as features for LSTM prediction. The results indicated that the predicted stock closing prices aligned more closely with the accurate closing prices compared to the traditional LSTM methods. Lastly, [PERSON] et al. (2017) presented a comprehensive study on the use of artificial neural networks (ANN) and support vector machines (SVM) for stock forecasting to provide further insights into the application of machine-learning techniques in the financial domain. [PERSON] et al. (2017) applied algorithms to social media and financial news data to assess their impact on stock-market prediction accuracy over a span of ten subsequent days. They conducted feature selection and minimized spam tweets in the datasets to enhance prediction quality. Furthermore, the study involved experiments to identify stock markets that were challenging to predict and those heavily influenced by social media and financial news. The researchers also compared the outcomes of various algorithms to determine a reliable classifier. The results recommended random forest for stock-trend prediction due to its consistent results in all the cases. Finally, deep-learning techniques were employed, and classifiers were combined to maximize prediction accuracy. The experimental findings revealed the highest prediction accuracies of 80.53% and 75.16% using social media and financial news data, respectively. [PERSON] et al. (2018) introduced a new system based on deep learning to predict the stock price. They combined LSTM and bidirectional gated recurrent unit (BiGRU) to predict the closing price of the stock market. Then, they applied the proposed method to some stocks and predicted their close price 10 and 30 min before the actual time. [PERSON] et al. (2018) have another study that considered news in the market price prediction. They developed a model based on TrellisNet and a sentiment attention mechanism (SA-TrellisNet) to predict stock-market prices. They integrated the LSTM and CNN models for sentiment analysis while employing a sentiment attention mechanism to allocate weights and a trellis network for stock prediction. The hybrid model includes three components: sentiment analysis, sentiment attention mechanism, and the prediction model.