LAUSR

The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, deep learning techniques promise a high degree of automation with generalized rule extraction for both text and sentiment classification tasks. This article aims to provide an empirical study on various deep neural networks (DNN) used for sentiment classification and its applications. In the preliminary step, the research carries out a study on several contemporary DNN models and their underlying theories. Furthermore, the performances of different DNN models discussed in the literature are estimated through the experiments conducted over sentiment datasets. Following this study, the effect of fine-tuning various hyperparameters on each model’s performance is also examined. Towards a better comprehension of the empirical results, few simple techniques from data visualization have been employed. This empirical study ensures deep learning practitioners with insights into ways to adapt stable DNN techniques for many sentiment analysis tasks.