@inproceedings{yeghaneh-abkenar-etal-2026-improving,
title = "Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features",
author = "Yeghaneh Abkenar, Mohammad and
Wang, Weixing and
Stede, Manfred and
Finlayson, Mark A. and
Picca, Davide and
Ioannidis, Panagiotis",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.760/",
doi = "10.63317/2e6be7q3bnzh",
pages = "9678--9691",
abstract = "Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward a specific target topic. While arguments{---}especially about controversial topics{---}often appeal to emotions, most prior work has not systematically incorporated explicit, fine-grained emotion analysis to improve performance on this task. In particular, prior research on stance classification has predominantly utilized non-argumentative texts and has been restricted to specific domains or topics, limiting generalizability. We work on five datasets from diverse domains encompassing a range of controversial topics and present an approach for expanding the Bias-Corrected NRC Emotion Lexicon using DistilBERT embeddings, which we feed into a Neural Argumentative Stance Classification model. Our method systematically expands the emotion lexicon through contextualized embeddings to identify emotionally charged terms not previously captured in the lexicon. Our expanded NRC lexicon (eNRC) improves over the baseline across all five datasets (up to +6.2 percentage points in F1 score), outperforms the original NRC on four datasets (up to +3.0), and surpasses the LLM-based approach on nearly all corpora. We provide all resources{---}including eNRC, the adapted corpora, and model architecture{---}to enable other researchers to build upon our work"
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<abstract>Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward a specific target topic. While arguments—especially about controversial topics—often appeal to emotions, most prior work has not systematically incorporated explicit, fine-grained emotion analysis to improve performance on this task. In particular, prior research on stance classification has predominantly utilized non-argumentative texts and has been restricted to specific domains or topics, limiting generalizability. We work on five datasets from diverse domains encompassing a range of controversial topics and present an approach for expanding the Bias-Corrected NRC Emotion Lexicon using DistilBERT embeddings, which we feed into a Neural Argumentative Stance Classification model. Our method systematically expands the emotion lexicon through contextualized embeddings to identify emotionally charged terms not previously captured in the lexicon. Our expanded NRC lexicon (eNRC) improves over the baseline across all five datasets (up to +6.2 percentage points in F1 score), outperforms the original NRC on four datasets (up to +3.0), and surpasses the LLM-based approach on nearly all corpora. We provide all resources—including eNRC, the adapted corpora, and model architecture—to enable other researchers to build upon our work</abstract>
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%0 Conference Proceedings
%T Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features
%A Yeghaneh Abkenar, Mohammad
%A Wang, Weixing
%A Stede, Manfred
%A Finlayson, Mark A.
%A Picca, Davide
%A Ioannidis, Panagiotis
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F yeghaneh-abkenar-etal-2026-improving
%X Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward a specific target topic. While arguments—especially about controversial topics—often appeal to emotions, most prior work has not systematically incorporated explicit, fine-grained emotion analysis to improve performance on this task. In particular, prior research on stance classification has predominantly utilized non-argumentative texts and has been restricted to specific domains or topics, limiting generalizability. We work on five datasets from diverse domains encompassing a range of controversial topics and present an approach for expanding the Bias-Corrected NRC Emotion Lexicon using DistilBERT embeddings, which we feed into a Neural Argumentative Stance Classification model. Our method systematically expands the emotion lexicon through contextualized embeddings to identify emotionally charged terms not previously captured in the lexicon. Our expanded NRC lexicon (eNRC) improves over the baseline across all five datasets (up to +6.2 percentage points in F1 score), outperforms the original NRC on four datasets (up to +3.0), and surpasses the LLM-based approach on nearly all corpora. We provide all resources—including eNRC, the adapted corpora, and model architecture—to enable other researchers to build upon our work
%R 10.63317/2e6be7q3bnzh
%U https://aclanthology.org/2026.lrec-1.760/
%U https://doi.org/10.63317/2e6be7q3bnzh
%P 9678-9691
Markdown (Informal)
[Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features](https://aclanthology.org/2026.lrec-1.760/) (Yeghaneh Abkenar et al., LREC 2026)
ACL