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

Lexicon-based approaches to sentiment analysis of text are based on each wordor lexical entry having a pre-defined weight indicating its sentiment polarity.These are usually manually assigned but the accuracy of these when comparedagainst machine leaning based approaches to computing sentiment, are not known.It may be that there are lexical entries whose sentiment values cause alexicon-based approach to give results which are very different to a machinelearning approach. In this paper we compute sentiment for more than 150,000English language texts drawn from 4 domains using the Hedonometer, alexicon-based technique and Azure, a contemporary machine-learning basedapproach which is part of the Azure Cognitive Services family of APIs which iseasy to use. We model differences in sentiment scores between approaches fordocuments in each domain using a regression and analyse the independentvariables (Hedonometer lexical entries) as indicators of each word's importanceand contribution to the score differences. Our findings are that the importanceof a word depends on the domain and there are no standout lexical entries whichsystematically cause differences in sentiment scores.

A Comparison of Lexicon-Based and ML-Based Sentiment Analysis: Are There Outlier Words?