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In this project, we will be using Internet News and Consumer Engagement dataset from Kaggle to predict top article and popularity score. We will be exploring our data to discover patterns, such as correlation, distribution, mean, and time series analysis. We will use both text regression and text classification models to predict engagement score and top article based on the title.
Photo by Obi Onyeador on Unsplash
Text classification is common among the application that we use on daily basis. For example, email providers use text classification to filter out spam emails from your inbox. The other most common use of text classification is in customer care where they use sentimental analysis to differentiate bad reviews from good reviews ADDI AI 2050. We are going to train our model on titles so that it can predict where the article is top or not. Text Regression is similar where we take text vectorized data and predict popularity score which is a decimal value.
Our key focus will be on an article title and how it affects other features.
This dataset (source) consists of data about news articles collected from Sept. 3, 2019 until Oct. 4, 2019. Afterwards, it is enriched by Facebook engagement data, such as number of shares, comments and reactions.
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