@article{article_683952, title={Stock Market Prediction by Combining Stock Price Information and Sentiment Analysis}, journal={International Journal of Advances in Engineering and Pure Sciences}, volume={33}, pages={18–27}, year={2021}, DOI={10.7240/jeps.683952}, author={Gumus, Adnan and Sakar, C. Okan}, keywords={Özyinelemeli Sinir Ağları,Uzun Kısa-Dönem Hafıza,Metin Sınıflandırma,Duygu Analizi,Teknik Analiz}, abstract={Predicting the stock market instrument price is a valuable but challenging machine learning task. Researchers use advanced techniques to improve the generalization ability of stock prediction models. However, considering that the stock market highly depends on the political and macroeconomic developments as well as the mood of the related investors, the models that use only stock prices fail to cover all factors affecting the stock market. Therefore, to improve the prediction accuracy of stock market prediction, in this study, we first apply sentiment analysis to the news related with the market and related stock, and then combine the sentiment labels of the news with stock prices and commonly used technical indicators. The obtained cumulative dataset is used to train a long short-term memory recurrent neural network, and the output of this regression model is used in the prediction of the closing price movement to decide whether the closing price next day will be higher. The experiments performed on 8-year data showed that while the f1 score of the model built without sentiment analysis was around 0.56, it has increased to 0.65 when stock prices are combined with sentiment labels. The results show that the model with sentiment labels fits better to the actual prices especially when there is a high volatility in the stock price.}, number={1}, publisher={Marmara Üniversitesi}