Benchmarking Machine Learning for Sentiment Analysis: A Case Study of News Articles in Multiple Languages

Sentiment analysis is the field of study that analyzes people’s opinions and sentiments towards entities such as products, services and organizations. Brand reputation analysis, competitive intelligence and social network analysis are just a few areas that can benefit from sentiment analysis. Most studies on sentiment analysis have only focused on domains like product reviews and social network content, leaving sentiment inference in the news domain under-investigated. In this work, we use a case study of a company specialized in the analysis of brand reputation to evaluate machine learning models for sentiment analysis on multilingual news articles. Several models were tested, including traditional machine learning models like KNN, and transformer-based models like BERT, Llama and GPT. The implemented models were evaluated on a dataset of Italian, German and Ladin news articles annotated with their sentiment polarity. Overall, our experiments show state-of-the-art results and confirm the outcomes of previous studies, i.e. that sentiment analysis of news articles remains a complex task. Machine learning systems can support manual annotators in accelerating the annotation process. Our findings can provide a benchmark for researchers in natural language processing when performing sentiment analysis of news articles.