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@article{150045,
        author = {Karan Sharma},
        title = {Forecasting Stock Prices using LSTM and Web Sentiment Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {2},
        pages = {207-210},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150045},
        abstract = {The craft of estimating the stock prices has been a troublesome task for huge numbers of researchers and analysts. Indeed, investors are profoundly interested in the exploration area of stock price prediction. For a good and fruitful investment, numerous investors are sharp in knowing the future circumstance of the stock market. An effective system for the stock market helps traders, investors, and analysts by giving strong information like the future direction of the stock market. In this work, I am presenting a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) approach to predict the stock market indices.},
        keywords = {Long short-term memory (LSTM), Recurrent neural network (RNN), nifty 50, root mean square error (RMSE), prediction, stock prices, web sentiment analysis},
        month = {},
        }