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

Eugenio Martínez-Cámara, Nuria Rodríguez Barroso, A. R. Moya, José Alberto Fernández, Elena Romero, Francisco Herrera
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引用次数: 9

Abstract

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.
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基于进化算法的Twitter情感分析深度学习超参数调优
情感分析的最新技术是由深度学习方法定义的,目前的研究工作主要集中在改进文本序列中潜在上下文信息的编码。然而,那些具有更高表示能力的神经网络越来越复杂,这意味着它们有更多的超参数,必须手工定义。我们认为超参数的设置可以定义为优化任务,因此我们声称进化算法可以用于优化深度学习方法的超参数。我们建议使用进化算法SHADE来优化Twitter中情感分析任务的深度学习模型的配置。我们在西班牙语推文语料库中评估了我们的提议,结果表明进化算法发现的超参数增强了深度学习方法的性能。
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