A Deep Learning Approach for Negation Detection from Product Reviews written in Spanish

Abstract

Online product reviews are becoming common and are being used more frequently by consumers to choose the most competitive products. Negation detection is a crucial task for information extraction from product review texts because negation can change the meaning of opinions given by consumers about products or services. Although several approaches have been proposed for negation detection in product reviews, research efforts have concentrated mainly on English. This paper describes a transformer-based approach for detecting negation in product reviews written in Spanish. This approach takes advantage of transfer learning techniques and uses a BERT-based model to perform negation detection. Performed tests using the SFU corpus for Spanish, showed an F1 score of 95.4% in the cue detection task and 91.5% in the scope resolution task. Our finding suggests that our BERT-based approach is feasible to perform negation detection in Spanish.