  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.
