Sentiment analysis: an empirical comparison between various training algorithms for artificial neural network

摘要

The proliferated increase in the commercial benefits of sentiment analysis accumulated a huge interest in the domain of sentiment classification. Sentiment analysis categorises a given text into positive or negative class. In this paper, we present an empirical comparison between different training algorithms gradient descent (GD), gradient descent with momentum backpropagation (GDM), gradient descent adaptive learning rate backpropagation (GDA), gradient descent with momentum and adaptive learning rate backpropagation (GDX), and Levenberg-Marquardt backpropagation (LM), used for training the neural network for the domain of sentiment classification. The performance of all the methods is compared and evaluated using three balanced binary datasets from various domains with different features using various performance metrics such as accuracy, precision, recall, f -score, mean squared error, and training time. The experiments are performed five times with different random seed values using 10-fold cross-validation. The results indicate that GDX and LM outperform other methods in terms of classification accuracy.