Sentiments Analysis of Social Media Text Using XGboost, LightGBM, and Fasttext Embeddings for Opinion Mining
| dc.contributor.author | Mohammed, S. B., Kadri, A. F., Abdulrahman T. A., Ilori, O., Tajudeen, K. O., Sulaiman, H. O., Ekundayo, O., & Babatunde A.N | |
| dc.date.accessioned | 2026-06-16T10:55:12Z | |
| dc.date.available | 2026-06-16T10:55:12Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Sentiment analysis is an important task in Natural Language Processing (NLP), aiming to classify text into categories such as positive, negative, or neutral. Despite considerable progress, existing models often struggle with the noisy, short-text data that characterizes social media platforms. Aim: This study proposes a sentiment classification framework built on the Sentiment140 dataset, where preprocessing steps and FastText embeddings were used to generate dense vector representations of tweets. Positive, negative, and a derived neutral class were defined as target categories. Method: Two gradient boosting algorithms, XGBoost and LightGBM, were trained and evaluated across five independent runs using standard metrics, including accuracy, precision, recall, and F1-score. Results: The results demonstrate that XGBoost consistently achieved superior performance, with an average accuracy of 97.8% ± 0.4 and all other metrics exceeding 97%, while LightGBM recorded substantially lower performance with an average accuracy of 75.2% ± 1.8. Baseline comparisons with majority-class prediction, Logistic Regression, and SVM confirmed the robustness of the proposed framework, while benchmarking against previously reported CNN and LSTM models showed a clear improvement over existing state-of-the-art result. These findings validate the effectiveness of combining FastText embeddings with XGBoost for sentiment classification and emphasize its reproducibility and robustness. The study provides methodological clarity, addresses the limitations of prior approaches, and establishes a reliable foundation for real-time applications in social media analytics, customer experience monitoring, and decision support systems. | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/7416 | |
| dc.language.iso | en | |
| dc.publisher | Published by: Faculty of Computing and Information Technology and Faculty of Natural and Applied Science, Sule lamido University, Kafin Hausa, Jigawa State, Nigeria. Available online at Sule Lamido University - Journal of Science and Technology. | |
| dc.title | Sentiments Analysis of Social Media Text Using XGboost, LightGBM, and Fasttext Embeddings for Opinion Mining | |
| dc.type | Article |