A Machine Learning Sentiment Analysis Approach on News Headlines to Evaluate the Performance of the Pakistani Government

Abstract

The growing amount of unstructured online data presents challenges in efficiently organizing and summarizing relevant information, hindering knowledge development and opinion-building on various topics. Sentimental analysis is key technique to understand public views, as news significantly influences people's perceptions and emotions on various subjects, including politics, economics, and art. The study assesses Pakistani governments' performance using machine learning sentiment analysis of news headlines scraped from Dawn news, focusing on, PMLN, and PTI political party regimes, which hold the government authorities in last ten years. This study uses machine learning and pre-trained models for textual representation, recording term context and semantics, and incorporating feature reduction to enhance sentiment analysis accuracy by selecting useful features and applying labels. The SVM and sentiment intensity analyser model performed well, in experiments on two news headline datasets, from which gaining accuracy on the dawn news dataset with the sentiment intensity analyser pre-trained model. The system evaluates government efficacy using predicted labelled news, displaying sentiment scores from headlines from four regimes containing that ranking them and assessing their impacts.