Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis

Sentiment analysis is the process of classifying natural lan-guage sentences as expressing positive or negative sentiments, and it is a crucial task where the explanation of a prediction might arguably be as necessary as the prediction itself. We analysed di fierent explanation techniques, and we applied them to the classification task of Sentiment Analysis. We explored how attention-based techniques can be exploited to extract meaningful sentiment scores with a lower computational cost than existing XAI methods.