A Comparative Study of NLP Systems for Sentiment Polarity Classification across Different Domains and Genres

Sentiment analysis systems are now among the most widely used tools across various sectors: from politics to stock markets, from marketing to communication, from the sports domain to medical and natural sciences, and from social media analysis to consumer preference evaluation. This study presents a performance comparison of different methodologies, techniques, and applications developed in recent years. We describe a concrete implementation of two distinct Natural Language Processing (NLP) systems for the sentiment polarity classification of Italian tweets and Amazon reviews. Two different classification systems were developed: the first employs an approach based on the explicit representation of the texts' linguistic features, while the second uses an approach based on non-interpretable vectors (embeddings). Finally, a study was conducted to understand which features are most relevant for classification, and the underlying causes that influence the systems' performance in both in-domain and out-of-domain scenarios are highlighted.