Financial data in banks are unstructured and complicated. It is challenging to analyze these texts manually due to the small amount of labeled training data in financial text. Moreover, the financial text consists of language in the economic domain where a general-purpose model is not efficient. In this thesis, data had collected from MFN (Modular Finance) financial news, this data is scraped and persisted in the database and price indices are collected from Bloomberg terminal. Comprehensive study and tests are conducted to find the state-of-art results for classifying the sentiments using traditional classifiers like Naive Bayes and transfer learning models like BERT and FinBERT. FinBERT outperform the Naive Bayes and BERT classifier. The time-series indices for sentiments are built, and their correlations with price indices calculated using Pearson correlation. Augmented Dickey-Fuller (ADF) is used to check if both the time series data are stationary. Finally, the statistical hypothesis Granger causality test determines if the sentiment time series helps predict price. This result shows that there is a significant correlation and causal relation between sentiments and price.