FINANCIAL-SENTIMENT-FUSION-COMBINING-FIQA-AND-FINANCIAL-PHRASEBANK-FOR-ENHANCED-ANALYSIS

This project, titled "Financial Sentiment Fusion: Combining FiQA and Financial PhraseBank for Enhanced Analysis," addresses the challenge of improving sentiment analysis in the financial domain by integrating two significant datasets: FiQA and Financial PhraseBank. The financial sector generates vast amounts of textual data through news, social media, and financial reports, where understanding sentiment is crucial for market predictions and investment strategies. The FiQA dataset provides a comprehensive resource for financial sentiment analysis with a focus on question-answer pairs, while the Financial PhraseBank offers a robust collection of financial phrases and their associated sentiments. By combining these datasets, this project aims to enhance sentiment classification accuracy, addressing the complexities of financial language and context. The project involves preprocessing and merging these datasets, followed by the development and evaluation of advanced machine learning models to classify sentiments more accurately. The goal is to create a functional classifier that provides nuanced insights into financial sentiments, offering valuable information for market analysis, investment decisions, and economic forecasting.