Predicting the popularity of news articles is an important task in the field of journalism and online media. By accurately predicting the number of shares an article is likely to receive, media companies can optimize their content distribution strategies and reach a wider audience.
There are several approaches to predicting the popularity of news articles, including machine learning algorithms, natural language processing techniques, and statistical analysis.
One common approach is to use machine learning algorithms, such as regression or decision trees, to analyze features of the articles, such as the headline, the length of the article, the author, and the topics covered, to predict the number of shares. The algorithms can be trained on a dataset of articles and their corresponding share counts to learn patterns in the data.
article_id: Unique identifier for article
title: Title of the news article
text: Content of the article
published_date: date on which the article was published
unique_tokens_rate: Rate of unique words in the content
num_hrefs: number of links in the article
num_imgs: number of images in the article
num_videos: number of videos in the article
average_token_length: Average length of the words in the content
num_keywords: number of keywords in metadata
data_channel: data channel of the article
min_avg_key: minimum shares for average keywords
max_avg_key: maximum shares for average keywords
avg_avg_key: average shares for average keywords
href_avg_shares: average shares for links in the article
global_subjectivity: text subjectivity
global_sentiment_polarity: text sentiment polarity
global_rate_positive_words: rate of positive words in the text
global_rate_negative_words: rate of negative words in the text
title_subjectivity: title subjectivity
title_sentiment_polarity: title sentiment polarity
shares: number of shares for the article (target variable)
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