Breaking the State of the Art in dialects of Spanish Sentiment Analysis
Daniel Palomino · 2020
Recent advances in neural language models such as ULMFit and BERT have shown impressive results on several Natural Language Processing (NLP) Tasks. However, this same performance for the Spanish language has not been equaled until now. In this work, we present a combined approach based on training a BERT language model within a modified ULMFit pipeline that allows us to obtain state-of-the-art results Spanish sentiment analysis for several dialects. In order to reinforce our approach we have performed our tests on relevant challenges (Martinez-Camara et al., 2017; 2018).