# Towards Target-dependent Sentiment Classification in News Articles

arXiv: https://arxiv.org/abs/2105.09660 · HTML: https://mlsanity.com/paper/2105.09660/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification](https://mlsanity.com/paper/1908.11860/) (2019)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [SemEval-2016 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.01973/) (2019)
- [Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1906.04501/) (2019)
- [The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity](https://mlsanity.com/paper/2005.14024/) (2020)

## Built on by (most cited first, 3 of 3)

- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts](https://mlsanity.com/paper/2305.17679/) (2023)
- [Robust Bias Detection in MLMs and its Application to Human Trait Ratings](https://mlsanity.com/paper/2502.15600/) (2025)

## Abstract (arXiv)

Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to popular TSC domains, we find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation. In an extensive evaluation, we find that the state of the art in TSC performs worse on news articles than on other domains (average recall AvgRec = 69.8 on NewsTSC compared to AvgRev = [75.6, 82.2] on established TSC datasets). Reasons include incorrectly resolved relation of target and sentiment-bearing phrases and off-context dependence. As a major improvement over previous news TSC, we find that BERT's natural language understanding capabilities capture the less explicit sentiment used in news articles.