Much unstructured data has been produced with the growth of the Internet and social media. A significant volume of textual data includes users' opinions about products in online stores and social media. By exploring and categorizing them, helpful information can be acquired, including customer satisfaction, user feedback about a particular event, predicting the sale of a specific product, and other similar cases. In this paper, we present an approach for sentiment analysis with a deep learning model and use it to recommend products. A two-channel convolutional neural network model has been used for opinion mining, which has five layers and extracts essential features from the data. We increased the number of comments by applying the SMOTE algorithm to the initial dataset and balanced the data. Then we proceed to cluster the aspects. We also assign a weight to each cluster using tensor decomposition algorithms that improve the recommender system's performance. Our proposed method has reached 91.6% accuracy, significantly improved compared to previous aspect-based approaches.

翻译：随着互联网和社交媒体的发展，产生了大量非结构化数据。其中包含用户对在线商店和社交媒体中产品评论的显著文本数据。通过探索和分类这些数据，可以获取有用的信息，包括客户满意度、用户对特定事件的反馈、预测特定产品的销售情况以及其他类似案例。本文提出了一种基于深度学习模型的情感分析方法，并将其用于产品推荐。我们采用了一种双通道卷积神经网络模型进行观点挖掘，该模型包含五个层，能够从数据中提取关键特征。通过将SMOTE算法应用于初始数据集，我们增加了评论数量并平衡了数据。随后，我们进行特征聚类。此外，利用张量分解算法为每个聚类分配权重，从而提升了推荐系统的性能。我们提出的方法达到了91.6%的准确率，相较于以往基于特征的方法有显著提升。