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

Sentiment classification is a fundamental task in natural languageprocessing, assigning one of the three classes, positive, negative, or neutral,to free texts. However, sentiment classification models are highly domaindependent; the classifier may perform classification with reasonable accuracyin one domain but not in another due to the Semantic multiplicity of wordsgetting poor accuracy. This article presents a new Persian/Arabic multi-domainsentiment analysis method using the cumulative weighted capsule networksapproach. Weighted capsule ensemble consists of training separate capsulenetworks for each domain and a weighting measure called domain belonging degree(DBD). This criterion consists of TF and IDF, which calculates the dependencyof each document for each domain separately; this value is multiplied by thepossible output that each capsule creates. In the end, the sum of thesemultiplications is the title of the final output, and is used to determine thepolarity. And the most dependent domain is considered the final output for eachdomain. The proposed method was evaluated using the Digikala dataset andobtained acceptable accuracy compared to the existing approaches. It achievedan accuracy of 0.89 on detecting the domain of belonging and 0.99 on detectingthe polarity. Also, for the problem of dealing with unbalanced classes, acost-sensitive function was used. This function was able to achieve 0.0162improvements in accuracy for sentiment classification. This approach on AmazonArabic data can achieve 0.9695 accuracies in domain classification.

weighted CapsuleNet networks for Persian multi-domain sentiment analysis