A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview of TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented crosslinguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

文本情感分析（TEA）旨在提取和分析文本中用户的情感状态。各种深度学习（DL）方法发展迅速，并且已在音频、图像和自然语言处理等许多领域证明是成功的。这一趋势使得越来越多的研究人员在科研中从传统机器学习转向深度学习。在本文中，我们对基于深度学习方法的文本情感分析进行了综述。在介绍了情感分析的背景（包括情感定义、情感分类方法以及情感分析的应用领域）之后，我们总结了深度学习技术以及词/句表征学习方法。然后，我们根据文本结构和语言类型对现有的文本情感分析方法进行分类：面向文本的单语方法、面向文本对话的单语方法、面向文本的跨语言方法以及面向表情符号的跨语言方法。最后我们讨论了情感分析面临的挑战和未来的研究趋势。我们希望我们的综述将有助于读者理解文本情感分析和深度学习方法之间的关系，同时也促进文本情感分析的发展。