The financial industry generates vast amounts of news and data every day, which can significantly impact market sentiment and influence financial decisions. It is challenging to analyze and process this information manually, leading to errors and biases. Therefore, it is necessary to use advanced techniques such as natural language processing and deep learning to automate the process of sentiment analysis and accurately predict market trends.
The objective of this project is to develop a financial news sentiment analysis system using bidirectional LSTM to classify news articles into positive or negative sentiment categories.
The proposed system will use bidirectional LSTM to analyze and classify financial news articles based on their sentiment. Bidirectional LSTM is a type of recurrent neural network that can analyze and process sequences of data in both forward and backward directions, making it particularly effective in natural language processing tasks.The system will be trained on a dataset of financial news articles that have been labeled as positive, negative, or neutral. The system will employ several techniques to improve the accuracy of sentiment analysis, including data preprocessing, feature engineering, and model tuning. The system's performance will be evaluated using a separate test dataset to assess its accuracy and generalization performance. The system's performance will also be compared to traditional sentiment analysis models to evaluate its effectiveness.
The system is expected to achieve high accuracy in financial news sentiment analysis, which will help investors and financial institutions make informed decisions. The system will also be scalable, meaning it can handle large volumes of financial news data without compromising its accuracy.
The proposed financial news sentiment analysis system using bidirectional LSTM will help investors and financial institutions improve their sentiment analysis models and make more accurate investment decisions. It will be an essential tool for stockbrokers, financial advisors, and other professionals involved in financial markets.
https://huggingface.co/spaces/ameya123ch/Financial_Sentiment_Analysis