Enhancing Financial Trading Strategies: A Comprehensive Sentiment Analysis Pipeline

Sentiment analysis plays a crucial role in understanding market dynamics and making informed decisions in financial trading. This paper proposes a comprehensive sentiment analysis pipeline tailored to financial markets, integrating machine learning techniques and swarm intelligence for feature selection. The pipeline encompasses pre-processing, feature extraction, swarm-based feature selection, training, classification, and evaluation stages. Pre-processing involves text normalization techniques such as tokenization, lowercasing, and stop words removal. Feature extraction utilizes TF-IDF values and similarity measures to represent text data. Swarm intelligence algorithms are employed for feature selection, optimizing the subset of features based on classifier performance. Machine learning classifiers are trained on the selected features to predict sentiment labels, and evaluation metrics assess the pipeline’s performance. The effectiveness of the pipeline is demonstrated through experiments on financial datasets, highlighting the importance of parameter tuning and algorithmic optimization. This research contributes to advancing sentiment analysis in financial markets, providing a robust framework for decision-making in trading and investment.