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

This paper describes our sentiment classification system submitted to SemEval-2015 Task 10. In the message-level polarity classification subtask, we obtain the highest macroaveraged F1-scores on three out of six testing sets. Specifically, we build a two-stage classifier to predict the sentiment labels for tweets, which enables us to design different features for subjective/objective classification and positive/negative classification. In addition to n-grams, lexicons, word clusters, and twitter-specific features, we develop several deep learning methods to automatically extract features for the message-level sentiment classification task. Moreover, we propose a polarity boosting trick which improves the performance of our system.

Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets