# News Sentiment Analysis

arXiv: https://arxiv.org/abs/2007.02238 · HTML: https://mlsanity.com/paper/2007.02238/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Do we really have to filter out random noise in pre-training data for language models?](https://mlsanity.com/paper/2502.06604/) (2025)
- [A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers](https://mlsanity.com/paper/2311.01712/) (2023)

## Abstract (arXiv)

Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology. IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job, thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events that comprise of emotions - good, bad, neutral. Sentiment analysis is utilized to investigate human emotions present in textual information. This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news data set, which expresses the applicability and validation of the adopted approach.