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

Fine-grained argumentation mining goes beyond coarse-grained distinctions such as claim and premise, by delving deeper into the underlying strategies employed (e.g., the use of facts or values to persuade the audience). Despite the advancements brought about by pre-trained language models, the task remains challenging. We investigate whether auxiliary knowledge such as emotion and moral value lexicon features can improve the classification of fine-grained argumentation strategies. Our Neural Flair Transformer Classifier (NFTC), in its base form, fine-tunes a transformer-based document encoder (RoBERTa) for end-to-end argument component classification. Evaluated across four corpora from diverse domains spanning public participation, persuasive forums, product reviews, and student essays, NFTC consistently outperforms majority-voting and Qwen2.5-7B baselines, achieving competitive performance on all datasets. Moreover, gains are observed against a fine-tuned LLaMA-3-8B-Instruct model, regarded in prior work as a leading approach. Injecting additional knowledge into NFTC yields mixed effects: emotion and moral value features provide consistent gains in product reviews and persuasive forums, but not in the other two domains. Our findings suggest that the utility of subjective knowledge is domain and schema dependent.