Key Phrase Extraction & Applause Prediction

TL;DR

This paper presents a machine learning approach that uses word embeddings to analyze article styles and predict popularity based on user feedback like claps and tags, aiding writers in understanding content impact.

Contribution

It introduces a novel method combining word embeddings and machine learning to predict article popularity and style influence.

Findings

Effective prediction of article popularity using the proposed model

Identification of key stylistic features influencing user engagement

Demonstrated correlation between article style vectors and popularity metrics

Abstract

With the increase in content availability over the internet it is very difficult to get noticed. It has become an upmost the priority of the blog writers to get some feedback over their creations to be confident about the impact of their article. We are training a machine learning model to learn popular article styles, in the form of vector space representations using various word embeddings, and their popularity based on claps and tags.