Weightless Neural Networks for text classification using tf-idf

While Weightless Neural Networks (WNN) have been proven effective in Natural Language Processing (NLP) applications, they require the use of highly customized features as they work on binary inputs. However, recent advancements have brought methodologies able to adapt WNN to real numbers showing competitive results on many classification tasks, but they often struggle on sparse data. In this paper, we show that WNN can successfully use sparse linguistic features, like tf-idf, using appropriate transformations. We also show that WNN can be used to improve the performances of existing models for Mixed Language Sentiment Analysis and that it has competitive performances for news categorization.