Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features

TL;DR

This paper enhances neural stance classification on controversial topics by expanding an emotion lexicon with contextual embeddings, leading to improved performance across diverse datasets.

Contribution

It introduces a method to systematically expand an emotion lexicon using DistilBERT embeddings, improving stance classification accuracy on multiple datasets.

Findings

Expanded lexicon improves F1 scores up to +6.2 points

Outperforms original lexicon on four datasets

Surpasses LLM-based approaches on most datasets

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.