![]() We compare results of different algorithms to find a consistent classifier. ![]() Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Accurate stock market prediction is of great interest to investors however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data.
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