Financial NEWS Sentiment Analysis

Project Overview: This project focuses on conducting sentiment analysis on a dataset of financial news headlines from the perspective of retail investors. The primary goal is to assess the sentiments of these headlines, categorizing them into "positive," "negative," or "neutral" sentiments. Sentiment analysis is a critical tool in decision-making and risk assessment in financial contexts. The dataset, referred to as the Financial Phrase Bank, contains 4,840 annotated sentences and was supported by the Emil Aaltonen Foundation and the Academy of Finland.
Project Objectives: The key objectives of this project are as follows:
1. Analyze the sentiment of financial news headlines.
2. Assess the robustness of sentiment analysis models.
3. Understand the limitations of these models in a real-world financial context.
Project Workflow: The project workflow involves a series of data preprocessing steps, machine learning model training, and model evaluation. The main steps include:
1. Data Preprocessing: Text data is cleaned, tokenized, and transformed into numerical features.
2. Sentiment Labeling: Sentiments (positive, negative, neutral) are assigned to headlines.
3. Model Training: Two models, Tree Ensemble and XGBoost, are trained on the labeled data.
4. Model Evaluation: The models are evaluated using metrics like accuracy, recall, precision, and F1 score.
Key Findings and Limitations: The project findings reveal the following key points:
• Both models achieved reasonable overall accuracy, but their performance on the positive class (e.g., positive sentiment) is limited.
• The models exhibit low recall for the positive class, indicating that a significant number of positive cases are being missed.
• Precision for the positive class is relatively higher, indicating the models' ability to avoid false positives.
Limitations and Recommendations: The limitations of the project include class imbalance, the need for further feature engineering, and the potential sensitivity to variations in input data. To improve model performance, we recommend:
• Addressing class imbalance using oversampling or alternative techniques.
• Exploring more advanced feature engineering to capture richer information from the text.
• Conducting hyperparameter tuning to optimize model performance.
• Ensuring high data quality and addressing potential data noise.
Business Impact: Sentiment analysis on financial news data is a valuable tool for decision-makers in the finance industry. The limitations identified in this project highlight the importance of continuous refinement and improvement to achieve more reliable predictions. Recognizing these limitations, stakeholders can make informed decisions and understand the potential risks associated with the models' predictions.
Conclusion: This project provides valuable insights into the challenges and opportunities of sentiment analysis in the financial domain. While the models demonstrate promise, addressing their limitations is essential to enhance their performance and reliability. The project underscores the significance of robust sentiment analysis in supporting financial decision-making and risk assessment.