Identifying Multiple Categories for News Headlines using Text Analysis
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Abstract
As entire world is witnessing this digital transformation giving rise to change in every aspect of human life. At Present, news classification is one of the valuable tools to improve the accessibility, and efficiency of news articles, due to the availability of huge digital data demanding accurate classification for users easy. The focus of our article is to build a context classifier for the news categories and evaluate the sentiments of text-based headlines. The Experimental study is carried out on 15 News categories available in the ‘News_Category_Dataset_v2’ dataset from Kaggle. The results of our study reveal the sentiment of the text associated with these news categories using a polarity index, with values ranging from positive and negative.
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