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

Newspapers are a rich informational source. A headline of an article sparks an interest in the reader. So, news providing agencies tend to create catchy headlines to attract the reader’s attention onto them, and this is how sarcasm manages to find its way into news headlines. Sarcasm employs the use of words that carry opposite meaning with respect to what needs to be conveyed. This leads to the need of developing methods by which we can correctly predict whether a piece of text, or news for that matter, truthfully means what it says or is simply being sarcastic about it. Here, the authors have used a dataset containing 55,329 tuples consisting of news headlines from  The Onion  and the  Huffington Post , which was taken from Kaggle, on which they applied feature extraction techniques such as Count Vectorizer, TF-IDF, Hashing Vectorizer, and Global Vectorizer (GloVe). Then they applied seven classifiers on the obtained dataset. The experimental results showed that the highest accuracies among the ML models were 81.39% for LR model with Count Vectorizer, 79.2% for LR model with TF-IDF Vectorizer, and 78% for SVM model with Count Vectorizer. They also obtained the best accuracy of 90.7% using the Bi-LSTM Deep Learning Model. They have trained the seven models and compared them based on their respective accuracies and F1-Scores.

Comparison of Artificial Decision Techniques for Detection of Sarcastic News Headlines