# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 2 Literature Review  
A stock market is a place for publicly listed companies to trade stocks and other financial instruments; likewise, the price of shares is termed the stock price [46]. Initial studies (e.g., [47]) held a view of the stock market as stochastic and, hence, non-predictable. However, later studies (e.g., [22; 35; 48; 49]) argued that the stock market may be predictable to an extent when it is examined from a behavioral economics and socioeconomic theory of finance point of view. Therefore, many research studies have developed different models to predict stock prices and parameters in the stock market. These various approaches and techniques can be categorized into three main categories in terms of strategy: (i) technical analysis, (ii) fundamental analysis, and (iii) sentiment-based analysis [20; 22; 30]. Technical analysis is the most popular approach. It defines a couple of indicators, such as open price, close price, low price, high price, and trading volume, and applies mathematical and statistical techniques to structured data to predict the stock price based on trends in the past and present stock prices [22; 48; 50; 51; 52]. On the contrary, the fundamental analysis is concerned with the company that underlies the stock itself instead of the actual stock [53; 54; 55]. The data that are used by the fundamental analyst are usually unstructured and, thus, pose some challenges. However, this type of data has occasionally been shown to be a good predictor of stock-price movement [56]. Moreover, this approach is utilized by financial analysts daily, as it incorporates various factors, such as economic forecasts, the efficiency of management, business opportunities, and financial statements [57]. Fundamental analysis can, in essence, be defined as a method of finding a stock's inherent value based on financial analysis. Lastly, sentiment-based analysis is based on linguistic feature extraction (e.g., [58; 59; 60]). This approach has not been as popular, given the difficulty of developing reliable and efficient sentiment analysis tools. This is mainly due to the design complexity and relevant source selection (which is of utmost importance). Given the popularity of the technical analysis approach, many studies have employed different statistical and mathematical techniques to predict stock prices. These techniques can be generally categorized into three groups: (i) statistical model (STAT), (ii) evolutionary algorithm (EA), and (iii) machine learning (ML), including neural networks (NN). To utilize these techniques, decision-makers should decide on the type of target variable the model aims to predict. These variables can either be the stock price, stock direction, index price, or index direction. The stock price refers to an individual numerical value that the model believes the stock will be priced at soon. The stock direction refers to the direction (i.e., up or down) in which the stock price will move. Furthermore, the index could be another target variable. Unlike the case of stocks, where the data pertains to that of an individual stock, an index measures a section of the stock market by combining price data from multiple stocks. A few well-known indices are the S&P 500 and the Dow Jones Industrial Average. Exploring the existing literature reveals that diverse techniques have been used for stock-market prediction. Table 1 summarizes studies that have used various techniques for stock-market prediction. To compile this table, we utilized two prominent research databases, namely the Web of Science (WOS) and Scopus, which are known for their comprehensive coverage across various disciplines. Additionally, we supplemented our search by exploring the reference lists of selected studies not initially found in these databases. Our survey focused primarily on papers published after 2000, particularly those published within the last decade.