The surge of e-commerce reviews has presented a challenge in manually annotating the vast volume of reviews to comprehend their underlying aspects and sentiments. This research focused on leveraging weakly supervised learning to tackle aspect category learning and the sentiment classification of reviews. Our approach involves the generation of labels for both aspects and sentiments, employing the Snorkel framework of WSL, which incorporates aspect terms, review sentiment scores, and review ratings as sources of weak signals. This innovative strategy significantly reduces the laborious labeling efforts required for processing such extensive datasets. In this study, we deployed hybrid models, namely BiLSTM, CNN-BiLSTM, and CNN-LSTM, which harness multiple inputs, including review text, aspect terms, and ratings. Our proposed model employs two distinct loss functions: Binary Cross Entropy with Sigmoid Activation for Multi-Label Classification, enabling us to learn aspect Labels such as Quality, Usability, Service, Size, and Price, and Categorical Cross Entropy with Softmax Activations for Multi-Class Classification. Subsequently, we meticulously evaluate the performance metrics of these three implemented models, including Macro F1 score and Macro Precision. CNN & Bi-LSTM model attained 0.78 and 0.79 F1 scores on aspect and sentiment identification, respectively. The outcomes of this research are poised to make a substantial contribution to e-commerce platforms, offering an efficient and automated means to label and analyze vast troves of user reviews.

翻译：电子商务评论的激增对手动标注海量评论以理解其潜在方面和情感提出了挑战。本研究聚焦于利用弱监督学习来解决评论的方面类别学习与情感分类问题。我们的方法通过采用弱监督学习框架Snorkel生成方面和情感的标签，该框架将方面词、评论情感分数和评论评分作为弱信号来源。这一创新策略显著减少了处理如此庞大数据集所需的大量标注工作。在本研究中，我们部署了混合模型，包括BiLSTM、CNN-BiLSTM和CNN-LSTM，这些模型利用多种输入，如评论文本、方面词和评分。我们提出的模型采用两种不同的损失函数：用于多标签分类的二分类交叉熵与Sigmoid激活函数，使我们能够学习质量、可用性、服务、尺寸和价格等方面标签；以及用于多分类的分类交叉熵与Softmax激活函数。随后，我们细致评估了这三种实现模型的性能指标，包括宏观F1分数和宏观精确率。CNN-BiLSTM模型在方面识别和情感识别上分别达到了0.78和0.79的F1分数。本研究结果有望为电子商务平台做出重大贡献，提供一种高效自动化的方法用于标注和分析海量用户评论。