M arathi E mo E xplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource M arathi (2025)

Abstract: Marathi, the third most widely spoken language in India with over 83 million native speakers, remains significantly underrepresented in Natural Language Processing (NLP) research. While sentiment analysis has achieved substantial progress in high-resource languages such as English, Chinese, and Hindi, available Marathi datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations. To address this gap, we present a new annotated dataset of 10,762 Marathi sentences, each labeled with sentiment (positive, negative, or neutral), emotion (joy, anger, surprise, disgust, sadness, fear, or neutral), and a corresponding natural language justification. Justifications are written in English and generated using GPT-4 under a human-in-the-loop framework to ensure label fidelity and contextual alignment. Extensive experiments with both classical and transformer-based models demonstrate the effectiveness of the dataset for interpretable affective computing in a low-resource language setting, offering a benchmark for future research in multilingual and explainable NLP.

## introduction india is among the most linguistically diverse nations globally, with 22 constitutionally recognized languages and hundreds of regional dialects spoken across its vast geography. despite this diversity, nlp research has primarily concentrated on high-resource languages like english and hindi, leaving many regional languages underrepresented. this imbalance hinders equitable digital participation and the development of inclusive ai systems. marathi, spoken by over 83 million people foot_0 , is one of the most widely used indian languages but has received comparatively little attention in nlp research. the lack of comprehensive datasets and pretrained models for marathi limits the development of robust tools for information access, social media analysis, and digital governance in the language. example from our dataset मला हे च नाही समजल क कु णाला कु *चे भु ं कणे कसे आवडू शकते . translation: i don't understand how anyone can like a dog barking. label: negative & disgust justification: the phrase "भु ं कणे " (barking), used metaphorically to describe people, reflects contempt or disgust, indicating a strong emotional aversion. meanwhile, the rapid expansion of mobile connectivity and internet penetration in india has triggered an unprecedented rise in user-generated content, especially on platforms like twitter, @xcite . these platforms serve as active arenas for political discourse, social commentary, and personal expression, often articulated in regional languages such as marathi. this content is frequently emotionally charged, making it valuable for affective computing tasks like sentiment and emotion analysis. these tasks play a crucial role in applications such as public opinion mining, misinformation tracking, hate speech detection, and content moderation @xcite @xcite . while advances in these areas have been substantial for high-resource languages @xcite @xcite , progress in low-resource indian languages remains fragmented. notable contributions, including hindisentiwordnet @xcite , semeval datasets @xcite , and the hindimd corpus @xcite , have laid initial groundwork. although marathi is widely spoken, it lacks high-quality annotated resources that capture both sentiment and fine-grained emotional expressions. a prominent dataset, l3cubemahasent @xcite , contains roughly 16,000 social-media texts labeled for sentiment, but it does not include emotion-level annotations or interpretability features such as natural-language justifications. prior work shows that sentiment-only labels often miss affective nuance, especially in subjective, sarcastic, or politically charged texts where distinguishing, for instance, anger versus sadness or fear versus surprise is crucial for accurate interpretation @xcite @xcite . for example, the marathi sentence मला हे च नाही समजल क कु णाला कु *चे भु ं कणे कसे आवडू शकते '' (i don't understand how anyone can like a dog barking) is labeled as negative and disgust. the justification identifies the term "भु ं क-णे " (barking) as a contemptuous metaphor, clarifying the emotional reading beyond simple polarity. hence, to address these gaps, this work introduces a new marathi dataset with sentiment, emotion, and sentence-level justifications for each instance. the justifications expose the reasoning behind label assignments, improving interpretability, transparency, and trust. while explanation-augmented resources exist for english, e.g., e-snli @xcite , @xcite , and eraser @xcite , comparable datasets are effectively absent for marathi. the main contributions of this work are as follows: further, this research is organised into the following sections: dataset construction and annotation, methodology and experimentation, and conclusion. ## conclusion this work presents an enriched multi-domain marathi dataset annotated with sentiment, emotion, and natural language justifications, aimed at advancing interpretable affective computing in lowresource language settings. by extending the existing l3cubemahasent corpus with tri-layer annotations, the dataset facilitates fine-grained emotion classification and supports explanation generation grounded in human-like reasoning. empirical evaluations demonstrate that transformer-based models, particularly those pretrained on multilingual corpora, significantly outperform traditional baselines across both sentiment and emotion tasks.