PROJECT
2019NYTimes Comments Analysis
Flatiron
Module project for Data Science Immersive program. Analyzed a dataset of New York Times articles and associated reader comments, with a specific focus on comments selected as "featured" by the publication. Primary topics of focus include natural language processing and classification.
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Overview
Description:
This data science project investigated the factors that influence New York Times editors' decisions to highlight certain user comments. The analysis examined approximately 2 million comments across thousands of articles to understand both content-based and non-content features that predict comment selection.
Project Goals:
Determine if there were statistically significant differences in article and comment patterns between Q1 2017 and Q1 2018
Build and evaluate NLP classification models to predict editor-selected comments based solely on comment content
Develop feature-based classification models to predict editor selections using non-text attributes
Identify key factors that influence the editorial highlighting process
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