Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features
This work provides an incremental improvement in argumentative stance classification for researchers and practitioners working with controversial topics, by systematically integrating fine-grained emotion features.
This paper addresses the problem of identifying the stance in argumentative texts, especially on controversial topics, by incorporating emotion analysis. The authors expanded the Bias-Corrected NRC Emotion Lexicon using DistilBERT embeddings and integrated these features into a neural stance classification model, achieving F1 score improvements of up to +6.2 percentage points over the baseline and outperforming LLM-based approaches on most datasets.
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.