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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Sentiment is expressed differently in different domains. For instance, words which are used in both domains can change their polarity. In this article, we present a study on these multi-polarity words. We first show that these multi-polarity words are actually used by automatic opinion classifiers and affect their performances: the classification performance improves if the multi-polarity words are considered as two different words, or even suppressed from the representation. In a second part, we present an exploratory method for multi-polarity words detection without any labelled target data. This method relies on the comparison of the co-occurrence profiles of the words with a set of pivot words, selected to have only one polarity. MOTS-CLES : Fouille d’opinion, Adaptation au domaine, Changement de polarite

Fouille d'opinion : ces mots qui changent de polarité selon le domaine