# Sentiment Analysis in the News

arXiv: https://arxiv.org/abs/1309.6202 · HTML: https://mlsanity.com/paper/1309.6202/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2013.

## Built on by (most cited first, 20 of 20)

- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [Named Entity Recognition and Classification on Historical Documents: A Survey](https://mlsanity.com/paper/2109.11406/) (2021)
- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles](https://mlsanity.com/paper/2110.07827/) (2021)
- [Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies](https://mlsanity.com/paper/2404.04361/) (2024)
- [Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection](https://mlsanity.com/paper/2308.12885/) (2023)
- [SumREN: Summarizing Reported Speech about Events in News](https://mlsanity.com/paper/2212.01146/) (2022)
- [Towards Target-dependent Sentiment Classification in News Articles](https://mlsanity.com/paper/2105.09660/) (2021)
- [Multi-task Learning for Cross-Lingual Sentiment Analysis](https://mlsanity.com/paper/2212.07160/) (2022)
- [LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma](https://mlsanity.com/paper/2403.00418/) (2024)
- [Sentiment Analysis of the COVID-related r/Depression Posts](https://mlsanity.com/paper/2108.06215/) (2021)
- [A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers](https://mlsanity.com/paper/2311.01712/) (2023)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://mlsanity.com/paper/2312.06053/) (2023)
- [Combining Objective and Subjective Perspectives for Political News Understanding](https://mlsanity.com/paper/2408.11174/) (2024)
- [Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis](https://mlsanity.com/paper/2409.20054/) (2024)
- [A Novel Two-stage Framework for Extracting Opinionated Sentences from News Articles](https://mlsanity.com/paper/2101.09743/) (2021)
- [Understanding peacefulness through the world news](https://mlsanity.com/paper/2106.00306/) (2021)
- [Quotations, Coreference Resolution, and Sentiment Annotations in Croatian News Articles: An Exploratory Study](https://mlsanity.com/paper/2212.07172/) (2022)
- [Profiling the news spreading barriers using news headlines](https://mlsanity.com/paper/2304.11088/) (2023)
- [CroSentiNews 2.0: A Sentence-Level News Sentiment Corpus](https://mlsanity.com/paper/2305.08187/) (2023)

## Abstract (arXiv)

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles - author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which (a) we test the relative suitability of various sentiment dictionaries and (b) we attempt to separate positive or negative opinion from good or bad news. In the experiments described here, we tested whether or not subject domain-defining vocabulary should be ignored. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.