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.

中文翻译：

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使用深度神经网络进行情感分析：比较研究和绩效评估

这十年见证了人工智能领域的显着发展，深度学习的革命改变了整个人工智能产业。最终，深度学习技术已成为当今计算世界中任何模型的重要组成部分。尽管如此，深度学习技术保证了文本和情感分类任务的通用规则提取的高度自动化。本文旨在对用于情感分类的各种深度神经网络 (DNN) 及其应用进行实证研究。在初步步骤中，该研究对几种当代 DNN 模型及其基础理论进行了研究。此外，文献中讨论的不同 DNN 模型的性能是通过在情感数据集上进行的实验来估计的。在这项研究之后，还检查了微调各种超参数对每个模型性能的影响。为了更好地理解实证结果，很少使用数据可视化中的简单技术。这项实证研究确保深度学习从业者深入了解如何将稳定的 DNN 技术应用于许多情感分析任务。