shubham-p-patil / stock_trend_prediction_using_setiment_analysis_of_news_data. Goto Github PK

This project forked from atharvabhave21/stock_trend_prediction_using_setiment_analysis_of_news_data.

I scrape the news data using webhose.io and stock price data using nsepy. then I labelled the data using information about rising or fall in prices corresponding to news scraped for that day. For the word embeddings, I used GloVe provided by Stanford University. I used stop words from NLTK to remove sentence fillers that do not change the context. Then I used TF-IDF to remove the words that provide the least information. then I used LSTM to map any time-dependent relations in the data set and predicted if a user should "buy" or "sell" the share and with what confidence based on today's news events.