Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

In recent years, the interest among the research community in sentiment analysis (SA) has grown exponentially. It is only necessary to see the number of scientific publications and forums or related conferences to understand that this is a field with great prospects for the future. On the other hand, the Twitter boom has boosted investigation in this area due fundamentally to its potential applications in areas such as business or government intelligence, recommender systems, graphical interfaces and virtual assistance. However, to fully understand this issue, a profound revision of the state of the art is first necessary. It is for this reason that this paper aims to represent a starting point for those investigations concerned with the latest references to Twitter in SA.