# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 2 Literature Review
### ML Techniques in Stock-Price Prediction
The authors also highlighted that incorporating textual data can improve prediction results. [PERSON] et al. [132] compared RF to NN and SVM and concluded that RF outperforms the others. However, it is worth noting that the applied NN was very simple, with only one hidden layer containing two neurons. Later, [PERSON] et al. [15] utilized RF as a critical part of training in a proprietary stock prediction model. They concluded that the relatively effective performance of their prediction model (in terms of accuracy and returns) is due to the incorporation of RF as one of the integrated models used as a learning method. Support vector machines (SVM) is another promising model in the domain of ML. This type of model separates data into distinct classes via a decision boundary and then seeks to maximize the margin. Due to the nonlinearity of stock-price data, SVM is an appropriate technique for prediction, as it can project the data points into a higher dimensional space by performing a function on the data that makes the classes linearly separable. As a result, many studies (e.g., [100; 135]) have utilized SVM for stock-price prediction. Additionally, several studies have successfully incorporated textual data into their SVMs for stock prediction [75; 91]. Nevertheless, the SVM algorithm is computationally time-consuming. Thus, some studies (e.g., [16]) utilized a kernel function to improve the efficiency of mapping data into a higher dimensional space. Similar to SVM, support vector regression (SVR) employs the same components and techniques; however, rather than classifying the mathematical operations, these operations are tasked with regressing the data points. [PERSON] et al. [79] utilized a multiple-kernel SVR to test whether or not the inclusion of news articles alongside technical indicators improved the predictive power of the SVR model. The results revealed that the multiple-kernel SVR outperformed a normal SVR model. The work of [PERSON] et al. [73] employed the AZfintext qualitative stock-price predictor and regressed stock quotes and financial news article data as inputs into an SVR algorithm for stock-price prediction. The authors examined whether incorporating sentiment-based analysis into the AZfintext system would improve the stock direction prediction accuracy. The results showed that incorporating sentiment analysis into the AZfintext system did not improve the overall prediction accuracy. Another widely employed method for stock-price prediction involves utilizing the auto-regressive integrated moving average (ARIMA) model, which is also commonly known as the Box-Jenkins model in finance. ARIMA is specifically designed for training in forecasting time-series data [136]. Functioning as a generalized random walk model, ARIMA is finely tuned to eliminate residual autocorrelation, which is a statistical measure of the correlation between variables based on past values. As a generalized exponential smoothing model, ARIMA can incorporate long-term trends and seasonality into its predictions [137]. In recent stock-price prediction research, ARIMA has frequently been integrated either into other ML algorithms or used as a benchmark for comparison. An early example is the work of [PERSON] and [PERSON] [66], who developed and tested a hybrid ARIMA and SVM model. They recognized ARIMA's declining popularity and demonstrated its utility in enhancing ML models. The results revealed that the proposed hybrid ARIMA-SVM model outperformed both the standalone ARIMA and SVM models. A subsequent study conducted by [PERSON] et al. [82] compared ARIMA with a three-layer NN and found that the NN consistently outperformed ARIMA in most cases. The graphical representation of their ARIMA predictions indicated a linear pattern, thereby emphasizing its limitation in providing value-based forecasting. Similarly, [PERSON] et al. [109] discovered that NNs significantly outperformed the benchmark autoregressive model in stock-price prediction.