Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages
In this digital world, people freely express their emotions using different social media platforms. As a result, modeling and integrating emotion-understanding models are vital for various human-computer interaction tasks such as decision-making, product and customer feedback analysis, political promotions, marketing research, and social media monitoring. As users express different emotions simultaneously in a single instance, annotating emotions in a multilabel setting such as the EthioEmo (Belay et al., 2025) dataset effectively captures this dynamic. Additionally, incorporating intensity, or the degree of emotion, is crucial, as emotions can significantly differ in their expressive strength and impact. This intensity is significant for assessing whether further action is necessary in decision-making processes, especially concerning negative emotions in applications such as healthcare and mental health studies. To enhance the EthioEmo dataset, we include annotations for the intensity of each labeled emotion. Furthermore, we evaluate various state-of-the-art encoder-only Pretrained Language Models (PLMs) and decoder-only Large Language Models (LLMs) to provide comprehensive benchmarking.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingDecision MakingDecoderEmotion RecognitionMarketingSimilar Papers 제목 키워드 기반
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning
Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extrac…
Emotion-Cause Pair ExtractionMulti-Label LearningEnhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning
Despite the widespread utilization of deep neural networks (DNNs) for speech emotion recognition (SER), they are severely restricted due to the paucity of labeled data for training. Recently, segment-based approaches for…
Emotion RecognitionSelf-LearningSpeech Emotion RecognitionEnhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation
Dynamic facial expression recognition (DFER) is a task that estimates emotions from facial expression video sequences. For practical applications, accurately recognizing ambiguous facial expressions -- frequently encount…
Data AugmentationDynamic Facial Expression RecognitionFacial Expression RecognitionBias in Emotion Recognition with ChatGPT
This technical report explores the ability of ChatGPT in recognizing emotions from text, which can be the basis of various applications like interactive chatbots, data annotation, and mental health analysis. While prior …
Emotion RecognitionSentiment AnalysisTask Vector in TTS: Toward Emotionally Expressive Dialectal Speech Synthesis
Recent advances in text-to-speech (TTS) have yielded remarkable improvements in naturalness and intelligibility. Building on these achievements, research has increasingly shifted toward enhancing the expressiveness of ge…
Speech Synthesis