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

As aspect-level sentiment labels are expensive and labor-intensive toacquire, zero-shot aspect-level sentiment classification is proposed to learnclassifiers applicable to new domains without using any annotated aspect-leveldata. In contrast, document-level sentiment data with ratings are more easilyaccessible. In this work, we achieve zero-shot aspect-level sentimentclassification by only using document-level reviews. Our key intuition is thatthe sentiment representation of a document is composed of the sentimentrepresentations of all the aspects of that document. Based on this, we proposethe AF-DSC method to explicitly model such sentiment composition in reviews.AF-DSC first learns sentiment representations for all potential aspects andthen aggregates aspect-level sentiments into a document-level one to performdocument-level sentiment classification. In this way, we obtain theaspect-level sentiment classifier as the by-product of the document-levelsentiment classifier. Experimental results on aspect-level sentimentclassification benchmarks demonstrate the effectiveness of explicit utilizationof sentiment composition in document-level sentiment classification. Our modelwith only 30k training data outperforms previous work utilizing millions ofdata.

Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition