Pandit Deendayal Energy University, Gandhinagar, India.
Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India.
Comput Intell Neurosci. 2022 Aug 23;2022:2645381. doi: 10.1155/2022/2645381. eCollection 2022.
Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.
情感分析是一种识别人们对特定目标（如人、活动、组织、服务、主题和产品）的态度、情感和情绪的方法。情感检测是情感分析的一个子集，因为它预测的是独特的情感，而不仅仅是表示积极、消极或中性。最近，许多研究人员已经在语音和面部表情识别方面进行了研究。然而，与语音不同，文本中的情感检测是一项繁琐的任务，因为缺少语音中的语调重音、面部表情、音高等线索。为了从文本中识别情感，过去已经提出了几种使用自然语言处理 (NLP) 技术的方法：关键词方法、基于词典的方法和机器学习方法。然而，关键词和基于词典的方法存在一些局限性，因为它们侧重于语义关系。在本文中，我们提出了一种混合（机器学习+深度学习）模型来识别文本中的情感。卷积神经网络 (CNN) 和 Bi-GRU 被用作深度学习技术。支持向量机被用作机器学习方法。使用三种不同类型的数据集（句子、推文和对话）组合来评估所提出方法的性能，其准确率达到 80.11%。
