Aspect-based sentiment analysis is of great importance and application because of its ability to identify all aspects discussed in the text. However, aspect-based sentiment analysis will be most effective when, in addition to identifying all the aspects discussed in the text, it can also identify their polarity. Most previous methods use the pipeline approach, that is, they first identify the aspects and then identify the polarities. Such methods are unsuitable for practical applications since they can lead to model errors. Therefore, in this study, we propose a multi-task learning model based on Convolutional Neural Networks (CNNs), which can simultaneously detect aspect category and detect aspect category polarity. creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews. We evaluated the proposed method using a Persian language dataset in the movie domain. Jacquard index and Hamming loss measures were used to evaluate the performance of the developed models. The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.

翻译：方面级情感分析因其能够识别文本中讨论的所有方面而具有重要性和应用价值。然而，只有当方面级情感分析在识别所有讨论方面的同时还能识别其极性时，才能发挥最大效力。以往多数方法采用流水线方式，即先识别方面再识别极性。此类方法不适合实际应用，因为它们可能导致模型误差。因此，在本研究中，我们提出一种基于卷积神经网络的多任务学习模型，该模型能够同时检测方面类别及其极性。单独构建一个模型可能无法提供最佳预测，并可能导致偏差和高方差等误差。为减少这些误差并提高模型预测效率，结合多个模型的集成学习可能产生更好的结果。因此，本文的主要目的是创建一个基于多任务深度卷积神经网络集成模型，以增强波斯语评论的情感分析。我们使用波斯语电影领域数据集对所提方法进行了评估。采用杰卡德指数和汉明损失度量来评估开发模型的性能。结果表明，这种新方法提高了波斯语情感分析模型的效率。