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

The social media time line, especially Twitter, is still interesting to follow. Various tweets delivered by the public are very informative and varied. This information should be able to be used further by utilizing the topic of conversation trends at one time. In this paper, the authors cluster the tweet data with the TF-IDF algorithm and the K-Mean method using the python programming language. The results of the tweet data clustering show predictions or possible topics of conversation that are being widely discussed by netizens. Finally, the data can be used to make decisions that utilize community sentiment towards an event through social media like Twitter.

Implementation of TF-IDF Algorithm and K-mean Clustering Method to Predict Words or Topics on Twitter