Deep Learning Hyper-parameter Tuning for Sentiment Analysis in Twitter based on Evolutionary Algorithms

The state of the art in Sentiment Analysis is defined by deep learning methods, and currently the research efforts are focused on improving the encoding of underlying contextual information in a sequence of text. However, those neural networks with a higher representation capacity are increasingly more complex, which means that they have more hyper-parameters that have to be defined by hand. We argue that the setting of hyper-parameters may be defined as an optimisation task, we thus claim that evolutionary algorithms may be used to the optimisation of the hyper-parameters of a deep learning method. We propose the use of the evolutionary algorithm SHADE for the optimisation of the configuration of a deep learning model for the task of sentiment analysis in Twitter. We evaluate our proposal in a corpus of Spanish tweets, and the results show that the hyper-parameters found by the evolutionary algorithm enhance the performance of the deep learning method.