• Trayushi Patel
• Shikha Pandey
• Yuvraj Pandey
• Yogesh Yadav
• Rajendra Arakh

Predicting market prices has been a topic of interest among both analysts and researchers for a long time. Market prices are hard to predict because of their high volatile nature which depends on diverse political and economic factors, change of leadership, investor sentiment, and many other factors. Predicting market prices based on either historical data or textual information alone has proven to be insufficient.Existing studies in sentiment analysis have found that there is a strong correlation between the movement of market prices and the publication of news articles. Several sentiment analysis studies have been attempted at various levels using algorithms such as support vector machines, naive Bayes regression, and deep learning. The accuracy of deep learning algorithms depends upon the amount of training data provided. However, the amount of textual data collected and analyzed during the past studies has been insufficient and thus has resulted in predictions with low accuracy.In our paper, we improve the accuracy of market price predictions by gathering a large amount of time series data and analyzing it in relation to related news articles, using deep learning models. The dataset we have gathered includes daily market prices for S&P500 companies for five years, along with more than 265,000 financial news articles related to these companies. Given the large size of the dataset, we use cloud computing as an invaluable resource for training prediction models and performing inference for a given market in real time.

market prediction, cloud, big data, machine learning, regression.

"Market Prediction Using News Sentiment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f723-f726, May-2024, Available :http://www.jetir.org/papers/JETIR2405580.pdf

"Market Prediction Using News Sentiment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf723-f726, May-2024, Available at : http://www.jetir.org/papers/JETIR2405580.pdf