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

A large amount of feedback was collected over the years. Many feedbackanalysis models have been developed focusing on the English language.Recognizing the concept of feedback is challenging and crucial in languageswhich do not have applicable corpus and tools employed in Natural LanguageProcessing (i.e., vocabulary corpus, sentence structure rules, etc). However,in this paper, we study a feedback classification in Mongolian language usingtwo different word embeddings for deep learning. We compare the results ofproposed approaches. We use feedback data in Cyrillic collected from 2012-2018.The result indicates that word embeddings using their own dataset improve thedeep learning based proposed model with the best accuracy of 80.1% and 82.7%for two classification tasks.

Deep learning model for Mongolian Citizens Feedback Analysis using Word Vector Embeddings