Seilsepour Azam, Ravanmehr Reza, Nassiri Ramin
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
J Supercomput. 2023 Jun 5:1-39. doi: 10.1007/s11227-023-05423-9.
Sentiment Analysis (SA) is a domain- or topic-dependent task since polarity terms convey different sentiments in various domains. Hence, machine learning models trained on a specific domain cannot be employed in other domains, and existing domain-independent lexicons cannot correctly recognize the polarity of domain-specific polarity terms. Conventional approaches of Topic Sentiment Analysis perform Topic Modeling (TM) and SA sequentially, utilizing the previously trained models on irrelevant datasets for classifying sentiments that cannot provide acceptable accuracy. However, some researchers perform TM and SA simultaneously using topic-sentiment joint models, which require a list of seeds and their sentiments from widely used domain-independent lexicons. As a result, these methods cannot find the polarity of domain-specific terms correctly. This paper proposes a novel supervised hybrid TSA approach, called Embedding Topic Sentiment Analysis using Deep Neural Networks (ETSANet), that extracts the semantic relationships between the hidden topics and the training dataset using Semantically Topic-Related Documents Finder (STRDF). STRDF discovers those training documents in the same context as the topic based on the semantic relationships between the Semantic Topic Vector, a newly introduced concept that encompasses the semantic aspects of a topic, and the training dataset. Then, a hybrid CNN-GRU model is trained by these semantically topic-related documents. Moreover, a hybrid metaheuristic method utilizing Grey Wolf Optimization and Whale Optimization Algorithm is employed to fine-tune the hyperparameters of the CNN-GRU network. The evaluation results demonstrate that ETSANet increases the accuracy of the state-of-the-art methods by 1.92%.
情感分析（SA）是一项依赖于领域或主题的任务，因为极性词在不同领域传达不同的情感。因此，在特定领域训练的机器学习模型不能用于其他领域，并且现有的与领域无关的词汇表不能正确识别特定领域极性词的极性。传统的主题情感分析方法依次执行主题建模（TM）和SA，利用在不相关数据集上预先训练的模型来对情感进行分类，而这些模型无法提供可接受的准确率。然而，一些研究人员使用主题 - 情感联合模型同时执行TM和SA，这需要来自广泛使用的与领域无关的词汇表的种子及其情感列表。结果，这些方法无法正确找到特定领域术语的极性。本文提出了一种新颖的有监督混合TSA方法，称为使用深度神经网络的嵌入主题情感分析（ETSANet），该方法使用语义主题相关文档查找器（STRDF）提取隐藏主题与训练数据集之间的语义关系。STRDF基于语义主题向量（一个新引入的涵盖主题语义方面的概念）与训练数据集之间的语义关系，发现与主题处于相同上下文中的那些训练文档。然后，通过这些与语义主题相关的文档训练一个混合CNN - GRU模型。此外，采用一种利用灰狼优化和鲸鱼优化算法的混合元启发式方法来微调CNN - GRU网络的超参数。评估结果表明，ETSANet将现有最先进方法的准确率提高了1.92%。
