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 humans, we can often detect from a persons utterances if he or she is infavor of or against a given target entity (topic, product, another person,etc). But from the perspective of a computer, we need means to automaticallydeduce the stance of the tweeter, given just the tweet text. In this paper, wepresent our results of performing stance detection on twitter data using asupervised approach. We begin by extracting bag-of-words to performclassification using TIMBL, then try and optimize the features to improvestance detection accuracy, followed by extending the dataset with two sets oflexicons - arguing, and MPQA subjectivity; next we explore the MALT parser andconstruct features using its dependency triples, finally we perform analysisusing Scikit-learn Random Forest implementation.

Performing Stance Detection on Twitter Data using Computational Linguistics Techniques