Geo-Political Sentiment Analysis using Machine Learning

This study presents the development of a recommendation-based automated system for geo-political sentiment analysis using machine learning techniques. The system processes large volumes of political text data, including tweets and news articles, by applying Natural Language Processing (NLP) methods such as tokenization, stop-word removal, and lemmatization to ensure clean and meaningful input. Feature extraction is carried out using techniques like TF-IDF, which convert textual data into numerical form suitable for model training. A supervised machine learning algorithm, specifically Support Vector Machine (SVM), is employed to classify sentiments into categories such as positive, negative, and neutral. The model is trained and optimized to achieve high accuracy and good generalization on unseen data. In addition, the system is integrated with a simple and user-friendly interface that allows users to input text and receive real-time sentiment predictions. Overall, the proposed approach provides an efficient and scalable solution for analyzing geo-political opinions, making it valuable for researchers, analysts, and decision-makers in understanding public sentiment trends.