The research focuses on evaluating various classical machine learning algorithms for sentiment classification in financial texts. A publicly available dataset combining FIQA and Financial PhraseBank sources was used, containing over 5,800 labeled financial sentences. Data preprocessing steps included cleaning, tokenization, stopword removal, and lemmatization. Exploratory data analysis was conducted to understand sentiment distribution, text length patterns, and word frequencies.
Sentiment labels were encoded numerically to serve as target variables in classification models. A range of traditional machine learning algorithms was implemented and assessed to explore their suitability for sentiment analysis in the financial domain. Evaluation metrics including accuracy, precision, recall, and F1-score were used to assess model performance.
Preliminary results indicate that traditional machine learning models can effectively classify sentiment in financial texts, especially when supported by proper text preprocessing. Furthermore, integrating sentiment analysis with financial data shows potential for improving market forecasting accuracy.


This work contributes to the growing field of financial data science by demonstrating the effectiveness of NLP-driven sentiment analysis and offering a framework for building predictive systems that combine textual and numerical financial indicators.
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