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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A review text is normally represented as a bag-of-words (BOW) in sentiment classification. Such a simplified BOW model has fundamental deficiencies in modeling some complex linguistic phenomena such as negation. In this work, we propose a dual-view co-training algorithm based on dual-view BOW representation for semisupervised sentiment classification. In dual-view BOW, we automatically construct antonymous reviews and model a review text by a pair of bags-of-words with opposite views. We make use of the original and antonymous views in pairs, in the training, bootstrapping and testing process, all based on a joint observation of two views. The experimental results demonstrate the advantages of our approach, in meeting the two co-training requirements, addressing the negation problem, and enhancing the semi-supervised sentiment classification efficiency.

Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation