indi D ata M iner at S em E val-2025 Task 11: From Text to Emotion: Transformer-Based Models for Emotions Detection in I ndian Languages (2025)

Abstract: Emotion detection is essential for applications like mental health monitoring and social media analysis, yet remains underexplored for Indian languages. This paper presents our system for SemEval-2025 Task 11 (Track A), focusing on multilabel emotion detection in Hindi and Marathi, two widely spoken Indian languages. We fine-tune IndicBERT v2 on the BRIGHTER dataset, achieving F1 scores of 87.37 (Hindi) and 88.32 (Marathi), outperforming baseline models. Our results highlight the effectiveness of fine-tuning a language-specific pretrained model for emotion detection, contributing to advancements in multilingual NLP research.

## introduction emotion detection has garnered significant interest among researchers due to its psychological, social, and commercial importance. people express emotions explicitly through words like happy or angry and implicitly through context, tone, or figurative language @xcite . the complexity and subjectivity of human emotions make their accurate identification challenging, especially in text-based scenarios. moreover, emotional expression varies significantly across languages and cultures, making it difficult to develop models that generalize well across linguistic boundaries @xcite . while extensive research has been conducted on emotion detection in high-resource languages such as english, spanish, german, and arabic @xcite @xcite , many low-resource languages, including those spoken in india, remain largely underexplored. recognizing this gap, @xcite introduced the brighter dataset, which covers 28 languages, primarily low-resource, spoken across africa, asia, eastern europe, and latin america. this dataset also includes two widely spoken indian languages, hindi and marathi. given india's rich linguistic diversity, understanding emotional expression in its various languages is essential for advancing multilingual nlp applications. this paper introduces our proposed approach and provides a comprehensive analysis of the task of semeval-2025 task 11 foot_0 (track a): bridging the gap in text-based emotion detection @xcite . although this task involves multilabel emotion detection across 28 widely used languages from diverse regions of the world, our system is specifically designed for emotion detection in hindi and marathi. since emotions are language-dependent, we leverage a pre-trained model specifically designed for indian languages. we fine-tune the indicbert v2 @xcite , a pre-trained model specifically trained on 23 indian languages to detect and classify emotions in hindi and marathi. our contributions focus on enhancing emotion detection for underrepresented languages to advance research in multilingual nlp. additionally, we investigate the effectiveness of pre-trained multilingual language models in emotion detection for the indian languages, including hindi and marathi languages. we conduct our experiments using the datasets provided by the organizers of semeval-2025 task 11 for track a. our results show that the proposed system achieves an f1 score of 87.37 for hindi and 88.32 for marathi, outperforming the baseline models. additionally, we perform an error analysis to evaluate the effectiveness of our system in detecting emotions in hindi and marathi. ## related works emotion detection in text is a key nlp task, enabling machines to interpret human emotions. lang text anger disgust fear joy sadness surprise hin mar अरे वाह! आज तो मे री बे ट ने अपने कमरे क ही नह , पू रे घर क सफाई खु द क है ! 0 0 0 1 0 1 बॉस ने आज मे री क्लास ले ली यार!! 0 0 0 0 1 0 म ने घर के पास एक लम्बे काले साँ प को दे खा, तब से दल क धड़कन बढ़ हुई ह । 0 0 1 0 0 0 सरकारच्या नण यामु ळे जनते ला होत असले ले नु कसान अत्यं त तरस्करणीय आहे . 1 1 0 0 0 0 घराची कामे करून माझा दवस सु रू होतो आ ण मला छान वाटते आहे . 0 0 0 1 0 0 हृदयाच्या खोलीला स्पश करणारी सं गीताची उदास सू र दु:ख आणखी वाढवते . 0 0 0 0 1 0 early studies highlighted the impact of emotions in written communication, similar to face-to-face interactions @xcite . emotion recognition has since been applied in healthcare, social media analysis, and conversational ai @xcite @xcite . a major challenge in emotion classification lies in distinguishing between expressed and perceived emotions @xcite . previous work explored multi-label classification of emotions in personal writings @xcite , using approaches like the multi-label maximum entropy model @xcite and rule-based methods with affect lexicons @xcite . deep learning advancements, including cnns with selfattention @xcite and cnn-lstm models @xcite , have improved fine-grained emotion detection. transformer-based models have further enhanced performance, especially in textual conversations @xcite . ## system overview and experimental setup we fine-tune and evaluate indicbert-v2 (doddapaneni et al., 2023) on the brighter dataset, using f1 score as the primary performance metric. additionally, to gain deeper insights into the model's performance across different emotions, we compute accuracy (acc), precision (prec), recall, and f1 score for each emotion. ## result and analysis we evaluate the performance of our fine-tuned emotion detection model for both hindi and marathi and compare it against several baseline multilingual language models, including labse, rembert, xlm-r, mbert, and mdeberta. the evaluation is based on the f1-score, which serves as the primary metric. the baseline model scores are directly taken from the brighter paper @xcite . to determine the interpretability of the system, we performed an attention heatmap analysis. we take three samples from each emotion class and visualize the attention weights to highlight the most influential words in the sentence. this helps in understanding how the model makes predictions by identifying keywords that contribute to each emotion classification. attention heatmap visualization for the hindi model is presented in figure 2 . by visualizing these attention weights, we verify that the model focuses on meaningful words while predicting emotions. due to space constraints, the attention heatmap visualization for the marathi model is provided in appendix figure 5 . ## conclusion and future work this study proposes a system for multilabel emotion detection in hindi and marathi languages. our proposed system fine-tuning indicbert v2 on the brighter dataset provided organize of semeval-2025 task 11. our experimental results suggest that our proposed systems outperformed baseline models. we also have several ablation studies to understand the misclassification of emotions between different emotion categories by plotting a confusion matrix and manual inspections of misclassified samples. our error analysis revealed that misclassification among emotion categories is due to confusion between similar emotions, misinterpretation of idiomatic expressions, and difficulty in capturing context-dependent emotions. to address these issues, future research will explore integrating knowledge extraction and narrative extraction, improving idiomatic phrase understanding, and incorporating multimodal cues to enhance emotion detection. additionally, we aim to extend this work beyond hindi and marathi to include other widely used indian languages such as tamil, bengali, telugu, and kannada.