Artificial Intelligence (AI) has been used for processing data to make
decisions, interact with humans, and understand their feelings and emotions.
With the advent of the internet, people share and express their thoughts on
day-to-day activities and global and local events through text messaging
applications. Hence, it is essential for machines to understand emotions in
opinions, feedback, and textual dialogues to provide emotionally aware
responses to users in today's online world. The field of text-based emotion
detection (TBED) is advancing to provide automated solutions to various
applications, such as businesses, and finances, to name a few. TBED has gained
a lot of attention in recent times. The paper presents a systematic literature
review of the existing literature published between 2005 to 2021 in TBED. This
review has meticulously examined 63 research papers from IEEE, Science Direct,
Scopus, and Web of Science databases to address four primary research
questions. It also reviews the different applications of TBED across various
research domains and highlights its use. An overview of various emotion models,
techniques, feature extraction methods, datasets, and research challenges with
future directions has also been represented.

该论文对 2005 年至 2021 年间 IEEE、Science Direct、Scopus 和 Web of Science 数据库中 63 篇文献进行了系统文献综述，总结了基于文本的情感检测技术在人工智能各个领域应用、情感模型、特征提取方法、数据集和未来研究方向等方面现有的研究进展和挑战。

基于文本的情感检测综述 -- 技术、应用、数据集和未来方向

A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions

While there have been significant advances in detecting emotions from speech
and image recognition, emotion detection on text is still under-explored and
remained as an active research field. This paper introduces a corpus for
text-based emotion detection on multiparty dialogue as well as deep neural
models that outperform the existing approaches for document classification. We
first present a new corpus that provides annotation of seven emotions on
consecutive utterances in dialogues extracted from the show, Friends. We then
suggest four types of sequence-based convolutional neural network models with
attention that leverage the sequence information encapsulated in dialogue. Our
best model shows the accuracies of 37.9% and 54% for fine- and coarse-grained
emotions, respectively. Given the difficulty of this task, this is promising.

本文介绍了一种基于文本的情感检测语料库及其在多方对话文本上对情感分类的深度学习模型，该模型优于现有方法。该文首先提出了一个新的语料库，提供了 Friends 节目中对话连续发言中七种情感的标注。作者提出了四种基于序列卷积神经网络的模型，其中最好的模型在细粒度和粗粒度情感的准确性分别达到了 37.9% 和 54%。