arXiv:2607. 05259v1 Announce Type: cross Abstract: Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications.
This paper introduces ViTOED, a novel dataset for target-oriented emotion detection in Vietnamese social media texts. The ViTOED comprises 10,985 user comments and 21,244 manually annotated opinion quadruples (source, target, expression, polarity) that follow strict guidelines.
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
arXiv:2608.21369v1 Announce Type: cross Abstract: Nigerian Pidgin is one of Africa's most widely spoken languages, yet remains severely underrepresented in language model evaluation. Existing benchma...
ViTOED is a new dataset for target‑oriented emotion detection in Vietnamese social media, containing 10,985 user comments and 21,244 manually annotated opinion quadruples (source, target, expression, polarity). The dataset uncovers Vietnamese‑specific linguistic phenomena such as implicit sources and targets and vocabulary ambiguities, and it serves as a benchmark for evaluating Vietnamese pre‑trained language models. A baseline using structured sentiment graphs shows that span detection and relation extraction remain challenging, indicating significant room for improvement in Vietnamese target‑oriented emotion detection tasks.
Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition proposes a hybrid framework that combines a Transformer and a Graph Attention Network to capture both global semantic information and fine-grained relationships between modalities. The model is evaluated on the IEMOCAP and MELD datasets, achieving weighted F1 scores of 72.45% and 77.37%, respectively, and surpasses state‑of‑the‑art methods. These results suggest that integrating multimodal features with balanced global and local context modeling can provide deeper emotional insights for dialogue emotion recognition.
The paper investigates affective framing in Bengali digital journalism by analyzing 300,000 news headlines with zero‑shot inference using Gemma 3 4B to estimate dominant emotions and overall affective tone. Results reveal frequent negative emotions—especially anger, sadness, disappointment, and fear—across the corpus, and a pilot validation on 200 headlines indicates the model’s estimates are useful yet not definitive benchmarks. The authors suggest a bias‑sensitive news interface that visualizes emotional cues to help readers detect affective framing patterns in daily news.
The YNU-HPCC team participated in Subtask A of SemEval‑2025 Task 11, "Bridging the Gap in Text‑Based Emotion," using a RoBERTa model with a single prediction head to process one emotion at a time. Their system achieved an official ranking score of 0.44 across all languages after translating the dataset into English with Google Translate. Analysis showed that a single head outperformed six simultaneous heads and that training on the uniformly translated English data improved results.
arXiv:2609.16997v1 Announce Type: new Abstract: Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapid...
arXiv:2601. 07565v2 Announce Type: replace-cross Abstract: Multimodal emotion understanding requires the integration of heterogeneous data sources, including text, audio, and visual modalities, while simultaneously addressing discrete emotion recognition and continuous sentiment analysis.
Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapidly. We introduce UNRESTSENT200K, a Bangla crisis...
arXiv:2403.17612v3 Announce Type: replace Abstract: Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus...