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

Eugenio Martínez Cámara, Nuria Rodríguez Barroso, Antonio R. Moya, José Alberto Fernández, Elena Romero, Francisco Herrera
2019 Proceedings of the 2019 Federated Conference on Computer Science and Information Systems  
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
more » ... 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.
doi:10.15439/2019f183 dblp:conf/fedcsis/CamaraBMFRH19 fatcat:vn6jyvoxjzginpaba7hsmlv5za