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Sentiment analysis is an essential task for interpreting subjective opinions and emotions in textual data, with significant implications across commercial and societal applications. This paper provides an overview of the shared task on Sentiment Analysis in Tamil and Tulu, organized as part of DravidianLangTech@NAACL 2025. The task comprises two components: one addressing Tamil and the other focusing on Tulu, both designed as multi-class classification challenges, wherein the sentiment of a given text must be categorized as positive, negative, neutral and unknown. The dataset was diligently organized by aggregating user-generated content from social media platforms such as YouTube and Twitter, ensuring linguistic diversity and real-world applicability. Participants applied a variety of computational approaches, ranging from classical machine learning algorithms such as Traditional Machine Learning Models, Deep Learning Models, Pre-trained Language Models and other Feature Representation Techniques to tackle the challenges posed by linguistic code-mixing, orthographic variations, and resource scarcity in these low resource languages.

NAACL 2025

Overview of the Shared Task on Sentiment Analysis in Tamil and Tulu

tulu-english

tamil-english

dravidian languages

code-mixed

sentiment analysis

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### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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Political multiclass detection is the task of identifying the predefined seven political classes. In this paper, we report an overview of the findings on the "Political Multiclass Sentiment Analysis of Tamil X(Twitter) Comments" shared task conducted at the workshop on DravidianLangTech@NAACL 2025. The participants were provided with annotated Twitter comments, which are split into training, development, and unlabelled test datasets. A total of 139 participants registered for this shared task, and 25 teams finally submitted their results. The performance of the submitted systems was evaluated and ranked in terms of the macro-F1 score.

Overview on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments: DravidianLangTech@NAACL 2025

Detecting and mitigating fake news on social media is critical for preventing misinformation, protecting democratic processes, preventing public distress, mitigating hate speech, reducing financial fraud, maintaining information reliability, etc. This paper summarizes the findings of the shared task "Fake News Detection in Dravidian Languages—DravidianLangTech@NAACL 2025." The goal of this task is to detect fake content in social media posts in Malayalam. It consists of two subtasks: the first focuses on binary classification (Fake or Original), while the second categorizes the fake news into five types—False, Half True, Mostly False, Partly False, and Mostly True. In Task 1, 22 teams submitted machine learning techniques like SVM, Naïve Bayes, and SGD, as well as BERT-based architectures. Among these, XLM-RoBERTa had the highest macro F1 score of 89.8%. For Task 2, 11 teams submitted models using LSTM, GRU, XLM-RoBERTa, and SVM. XLM-RoBERTa once again outperformed other models, attaining the highest macro F1 score of 68.2%.

Overview of the Shared Task on Fake News Detection in Dravidian Languages-DravidianLangTech@NAACL 2025

This study introduces a novel multilingual model designed to effectively address the challenges of detecting abusive content in low resource, code-mixed languages, where limited data availability and the interplay of mixed languages, leading to complex linguistic phenomena, create significant hurdles in developing robust machine learning models. By leveraging transfer learning techniques and employing multi-head attention mechanisms, our model demonstrates impressive performance in detecting abusive content in both Tamil and Malayalam datasets. On the Tamil dataset, our team achieved a macro F1 score of 0.7864, while for the Malayalam dataset, a macro F1 score of 0.7058 was attained. These results highlight the effectiveness of our multilingual approach, delivering strong performance in Tamil and competitive results in Malayalam.

Incepto@DravidianLangTech 2025: Detecting Abusive Tamil and Malayalam Text Targeting Women on YouTube

Sentiment analysis on social media platforms plays a crucial role in understanding public opinion and the decision-making process on political matters. As a significant number of individuals express their views on social media, analyzing these opinions is essential for monitoring political trends and assessing voter sentiment. However, sentiment analysis for low-resource languages, such as Tamil, presents considerable challenges due to the limited availability of annotated datasets and linguistic complexities. To address this gap, we utilize a novel dataset encompassing seven sentiment classes, offering a unique opportunity to explore sentiment variations in Tamil political discourse. In this study, we evaluate multiple pre-trained models from the Hugging Face library and experiment with various hyperparameter configurations to optimize model performance. Our findings aim to contribute to the development of more effective sentiment analysis tools tailored for low-resource languages, ultimately empowering Tamil-speaking communities by providing deeper insights into their political sentiments. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Eureka-Sentiment-Analysis-Tamil

Eureka-CIOL@DravidianLangTech 2025: Using Customized BERTs for Sentiment Analysis of Tamil Political Comments

The widespread spread of fake news on social media poses significant challenges, particularly for low-resource languages like Malayalam. The accessibility of social platforms accelerates misinformation, leading to societal polarization and poor decision-making. Detecting fake news in Malayalam is complex due to its linguistic diversity, code-mixing, and dialectal variations, compounded by the lack of large labeled datasets and tailored models. To address these, we developed a fine-tuned transformer-based model for binary and multiclass fake news detection. The binary classifier achieved a macro F1 score of 0.814, while the multiclass model, using multimodal embeddings, achieved a score of 0.1978. Our system ranked 14th and 11th in the shared task competition, highlighting the need for specialized techniques in underrepresented languages. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Akatsuki-Fake-News-Detection.

Akatsuki-CIOL@DravidianLangTech 2025: Ensemble-Based Approach Using Pre-Trained Models for Fake News Detection in Dravidian Languages

Online product reviews influence customer choices and company reputations. However, companies can counter negative reviews by generating fake reviews that portray their products positively. These fake reviews lead to legal disputes and concerns, particularly because AI detection tools are limited in low-resource languages such as Tamil and Malayalam. To address this, we use machine learning and deep learning techniques to identify AI-generated reviews. We utilize Tamil BERT and Malayalam BERT in the embedding layer to extract contextual features. These features are sent to a Feedforward Neural Network (FFN) with softmax to classify reviews as AI-generated or not. The performance of the model is evaluated on the dataset. The results show that the transformer-based embedding achieves a better accuracy of 91.98% on Tamil data and an accuracy of 87.50% on Malayalam data.

Team-Risers@DravidianLangTech 2025: AI-Generated Product Review Detection in Dravidian Languages Using Transformer-Based Embeddings

This study addresses the challenge of fake news detection in code-mixed and transliterated text, focusing on a multilingual setting with significant linguistic variability. A novel approach is proposed, leveraging a fine-tuned multilingual transformer model trained using Masked Language Modeling on a dataset that includes original, fully transliterated, and partially transliterated text. The fine-tuned embeddings are integrated into a custom transformer classifier designed to capture complex dependencies in multilingual sequences. The system achieves state-of-the-art performance, demonstrating the effectiveness of combining transliteration-aware fine-tuning with robust transformer architectures to handle code-mixed and resource-scarce text, providing a scalable solution for multilingual natural language processing tasks.

byteSizedLLM@DravidianLangTech 2025: Fake News Detection in Dravidian Languages Using Transliteration-Aware XLM-RoBERTa and Transformer Encoder-Decoder

This research introduces an innovative Attention BiLSTM-XLM-RoBERTa model for tackling the challenge of fake news detection in Malayalam datasets. By fine-tuning XLM-RoBERTa with Masked Language Modeling (MLM) on transliteration-aware data, the model effectively bridges linguistic and script diversity, seamlessly integrating native, Romanized, and mixed-script text. Although most of the training data is monolingual, the proposed approach demonstrates robust performance in handling diverse script variations. Achieving a macro F1-score of 0.5775 and securing top rankings in the shared task, this work highlights the potential of multilingual models in addressing resource-scarce language challenges and sets a foundation for future advancements in fake news detection.

byteSizedLLM@DravidianLangTech 2025: Fake News Detection in Dravidian Languages Using Transliteration-Aware XLM-RoBERTa and Attention-BiLSTM

This research presents a robust multimodal framework for hate speech detection in Malayalam, combining fine-tuned Wav2Vec 2.0, Malayalam-Doc-Topic-BERT, and an Attention-Driven BiLSTM architecture. The proposed approach effectively integrates acoustic and textual features, achieving a macro F1-score of 0.84 on the Malayalam test set. Fine-tuning Wav2Vec 2.0 on Malayalam speech data and leveraging Malayalam-Doc-Topic-BERT significantly improved performance over prior methods using openly available models. The results highlight the potential of language-specific models and advanced multimodal fusion techniques for addressing nuanced hate speech categories, setting the stage for future work on Dravidian languages like Tamil and Telugu.

byteSizedLLM@DravidianLangTech 2025: Multimodal Hate Speech Detection in Malayalam Using Attention-Driven BiLSTM, Malayalam-Topic-BERT, and Fine-Tuned Wav2Vec 2.0

This study presents a hybrid model integrating TamilXLM-RoBERTa and MalayalamXLM-RoBERTa with BiLSTM and attention mechanisms to classify AI-generated and human-written product reviews in Tamil and Malayalam. The model employs a transliteration-based fine-tuning strategy, effectively handling native, Romanized, and mixed-script text. Despite being trained on a relatively small portion of data, our approach demonstrates strong performance in distinguishing AI-generated content, achieving competitive macro F1 scores in the DravidianLangTech 2025 shared task. The proposed method showcases the effectiveness of multilingual transformers and hybrid architectures in tackling low-resource language challenges.

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Overview on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments: DravidianLangTech@NAACL 2025

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Overview of Second Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu

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