English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The surge of e-commerce reviews has presented a challenge in manuallyannotating the vast volume of reviews to comprehend their underlying aspectsand sentiments. This research focused on leveraging weakly supervised learningto 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, reviewsentiment scores, and review ratings as sources of weak signals. Thisinnovative strategy significantly reduces the laborious labeling effortsrequired for processing such extensive datasets. In this study, we deployedhybrid models, namely BiLSTM, CNN-BiLSTM, and CNN-LSTM, which harness multipleinputs, including review text, aspect terms, and ratings. Our proposed modelemploys two distinct loss functions: Binary Cross Entropy with SigmoidActivation for Multi-Label Classification, enabling us to learn aspect Labelssuch as Quality, Usability, Service, Size, and Price, and Categorical CrossEntropy with Softmax Activations for Multi-Class Classification. Subsequently,we meticulously evaluate the performance metrics of these three implementedmodels, including Macro F1 score and Macro Precision. CNN & Bi-LSTM modelattained 0.78 and 0.79 F1 scores on aspect and sentiment identification,respectively. The outcomes of this research are poised to make a substantialcontribution to e-commerce platforms, offering an efficient and automated meansto label and analyze vast troves of user reviews.

Aspect category learning and sentimental analysis using weakly supervised learning