Sirimevan, NMamalgaha, I. G. U. HJayasekara, CMayuran, Y. SJayawardena, C2022-01-062022-01-062019-12-05Cited by 7978-1-7281-4170-1https://rda.sliit.lk/handle/123456789/476Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value. For this, LSTM - RNN models were employed to analyze sperate sources and Ensembled method with Weighted Average and Differential Evolution technique were used for more accurate prediction of the stock prices. And highly accurate predictions were made to one-day, seven-days, 15-days and 30 days for the future. So that investors could gain an insight into what they are inventing for and the companies to track how well they will perform in the stock market.enStock market predictionSentiment AnalysisNeural NetworksLong-short Term Memory Neural NetworksEnsemble MethodWeigthed AverageDow jonesArticle10.1109/ICAC49085.2019.9103381