Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech
The stock market is highly influenced by news and investor sentiment, making trend prediction both challenging and valuable. This project develops a framework for stock price trend prediction by integrating sentiment analysis of financial news with historical market data. News headlines are clean...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/6957/1/fyp_DE_2025_CJK.pdf http://eprints.utar.edu.my/6957/ |
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| _version_ | 1854094445384826880 |
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| author | Cherng, Jun Kai |
| author_facet | Cherng, Jun Kai |
| author_sort | Cherng, Jun Kai |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The stock market is highly influenced by news and investor sentiment, making trend prediction
both challenging and valuable. This project develops a framework for stock price trend
prediction by integrating sentiment analysis of financial news with historical market data. News
headlines are cleaned and analyzed using VADER, TextBlob, BERT, and FinBERT to generate
sentiment scores, which are merged with OHLCV price data and enriched with lagged returns
and time-based features. Five machine learning models — Support Vector Machine (SVM),
Logistic Regression (LR), Random Forest (RF), XGBoost, and LightGBM — are trained and
tuned using walk-forward cross-validation. Their performance is evaluated using accuracy,
precision, recall, F1-score, and confusion matrices, with XGBoost achieving the best results.
Finally, a Power BI dashboard is built to visualize sentiment trends, market data, and model
predictions, making insights interactive and actionable. Results show that incorporating
sentiment features improves predictive performance, supporting data-driven decision-making
for investors and analysts. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6957 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.69572025-12-28T10:43:24Z Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech Cherng, Jun Kai T Technology (General) TD Environmental technology. Sanitary engineering The stock market is highly influenced by news and investor sentiment, making trend prediction both challenging and valuable. This project develops a framework for stock price trend prediction by integrating sentiment analysis of financial news with historical market data. News headlines are cleaned and analyzed using VADER, TextBlob, BERT, and FinBERT to generate sentiment scores, which are merged with OHLCV price data and enriched with lagged returns and time-based features. Five machine learning models — Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGBoost, and LightGBM — are trained and tuned using walk-forward cross-validation. Their performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices, with XGBoost achieving the best results. Finally, a Power BI dashboard is built to visualize sentiment trends, market data, and model predictions, making insights interactive and actionable. Results show that incorporating sentiment features improves predictive performance, supporting data-driven decision-making for investors and analysts. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6957/1/fyp_DE_2025_CJK.pdf Cherng, Jun Kai (2025) Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech. Final Year Project, UTAR. http://eprints.utar.edu.my/6957/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Cherng, Jun Kai Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech |
| title | Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech |
| title_full | Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech |
| title_fullStr | Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech |
| title_full_unstemmed | Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech |
| title_short | Sentiment analysis of financial news for predicting stock price trends using NLP techniques in fintech |
| title_sort | sentiment analysis of financial news for predicting stock price trends using nlp techniques in fintech |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6957/1/fyp_DE_2025_CJK.pdf http://eprints.utar.edu.my/6957/ |
| url_provider | http://eprints.utar.edu.my |