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

In today's interconnected and multilingual world, code-mixing of languages onsocial media is a common occurrence. While many Natural Language Processing(NLP) tasks like sentiment analysis are mature and well designed formonolingual text, techniques to apply these tasks to code-mixed text stillwarrant exploration. This paper describes our feature engineering approach tosentiment analysis in code-mixed social media text for SemEval-2020 Task 9:SentiMix. We tackle this problem by leveraging a set of hand-engineeredlexical, sentiment, and metadata features to design a classifier that candisambiguate between "positive", "negative" and "neutral" sentiment. With thismodel, we are able to obtain a weighted F1 score of 0.65 for the "Hinglish"task and 0.63 for the "Spanglish" tasks

C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering