Search papers, labs, and topics across Lattice.
This paper introduces the ViTOED dataset, which consists of 10,985 user comments and 21,244 annotated opinion quadruples specifically designed for target-oriented emotion detection in Vietnamese social media. The dataset highlights unique Vietnamese linguistic phenomena, such as implicit sources and vocabulary ambiguities, which are crucial for understanding user emotions towards entities. Empirical evaluations using structured sentiment graphs and various pre-trained Vietnamese language models reveal significant challenges in span detection and relation extraction, indicating a need for further advancements in this area.
Vietnamese social media sentiment analysis faces unique challenges, with implicit sources and vocabulary ambiguities complicating emotion detection.
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. The dataset reveals Vietnamese-specific phenomena, such as implicit sources and targets and vocabulary ambiguities, enabling deeper analysis of user emotions toward entities. We propose a baseline using structured sentiment graphs and evaluate various Vietnamese pre-trained language models. The empirical results highlight challenges in span detection and relation extraction and indicate substantial room for model improvement in Vietnamese Target-Oriented Emotion Detection tasks.
