Towards Target-Dependent Sentiment Classification in News Articles

Abstract

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 current 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

Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)

“…In our work, we employ the target entity extraction method for the BERT-based model, but we simplify the extraction by using only target embeddings. Similar BERT-based approaches were adapted for the targeted sentiment analysis in the news domain on a sentence (Hamborg et al, 2021) and headline (Salgueiro et al, 2022) level for English and Spanish language, respectively.…”

NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

“…To our knowledge, there exist two datasets for evaluation of news TSC methods (Steinberger et al, 2017), which -perhaps due to its small size (N = 1274) -has not been used or tested in recent TSC literature. Recently, Hamborg et al (2021) proposed a dataset (N = 3002) used to explore target-dependent sentiment in news articles. The dataset suffers from various shortcomings, particularly its small size, class imbalance, and lacking the more ambiguous and implicit types of sentiment expressions described above.…”

“…In a previous short-paper, we explored the characteristics of how sentiment is expressed in news articles by creating and analyzing a small-scale TSC dataset (Hamborg et al, 2021). The paper at hand addresses our former exploratory work's critical findings, including essential improvements to the dataset.…”

“…Further, we devise annotation instructions specifically created to capture a broad spectrum of sentiment expressions specific to news articles. In contrast, the early dataset misses the more implicit sentiment expressions commonly used by news authors (Hamborg et al, 2021;Steinberger et al, 2017). Also, we comprehensively test various consolidation strategies and conduct an expert annotation to validate the dataset.…”

YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

“…Other TSA datasets on Twitter data include targets that are either celebrities, products, or companies (Dong et al, 2014), and a multi-target corpus on UK elections data (Wang et al, 2017a). Lastly, Hamborg et al (2021) annotated named entities for their sentiment within the news domain.…”