GenAI4ELab/papercli-papers huggingface dataset:
LexiLogic@DravidianLangTech 2025: Political Multiclass Sentiment Analysis of Tamil X(Twitter) Comments and Sentiment Analysis in Tamil and Tulu
Authors: Billodal Roy; Souvik Bhattacharyya; Pranav Gupta; Niranjan Kumar M
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: We present our approach and findings for two sentiment analysis shared tasks as part of DravidianLangTech@NAACL 2025. The first task involved a seven-class political sentiment classification for Tamil tweets, while the second addressed code-mixed sentiment analysis in Tamil-English and Tulu-English social media texts. We employed language-specific BERT models fine-tuned on the respective tasks, specifically utilizing the L3Cube-Tamil-BERT for Tamil classification and a Telugu-based BERT model for Tulu classification. Our system achieved notable results, particularly securing the first position in the Tulu code-mixed sentiment analysis track. The experiments demonstrate the effectiveness of language-specific pre-trained models for Dravidian language sentiment analysis, while also highlighting the challenges in handling political discourse and code-mixed content.
Detection of Religious Hate Speech During Elections in Karnataka
Authors: Msvpj Sathvik; Raj Sonani; Ravi Teja Potla
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: We propose a novel dataset for detecting religious hate speech in the context of elections in Karnataka, with a particular focus on Kannada and Kannada-English code-mixed text. The data was collected during the Karnataka state elections and includes 3,000 labeled samples that reflect various forms of online discourse related to religion. This dataset aims to address the growing concern of religious intolerance and hate speech during election periods, it’s a dataset of multilingual, code-mixed language. To evaluate the effectiveness of this dataset, we benchmarked it using the latest state-of-the-art algorithms. We achieved accuracy of 78.61%.
DLTCNITPY@DravidianLangTech 2025 Abusive Code-mixed Text Detection System Targeting Women for Tamil and Malayalam Languages using Deep Learning Technique
Authors: Habiba A; Aghila G
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The growing use of social communication platforms has seen women facing higher degrees of online violence than ever before. This paper presents how a deep learning abuse detection system can be applied to inappropriate text directed at women on social media. Because of the diversity of languages and the casual nature of online communication, coupled with the cultural diversity around the world, the detection of such content is often severely lacking. This research utilized Long Short-Term Memory (LSTM) for abuse text detection in Malayalam and Tamil languages. This modeldelivers 0.75, a high F1 score for Malayalam, and for Tamil, 0.72, achieving the desired balance of identifying abuse and non-abusive content and achieving high-performance rates. The designed model, based on the dataset provided in DravidianLangTech@NAACL2025 (shared task) comprising code-mixed abusive and nonabusive social media posts in Malayalam and Tamil, showcases a high propensity for detecting accuracy and indicates the likely success of deep learning-based models for abuse textdetection in resource-constrained languages.
TSD: Towards Computational Processing of Tamil Similes - A Tamil Simile Dataset
Authors: Aathavan Nithiyananthan; Jathushan Raveendra; Uthayasanker Thayasivam
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: A simile is a powerful figure of speech that makes a comparison between two different things via shared properties, often using words like “like” or “as” to create vivid imagery, convey emotions, and enhance understanding. However, computational research on similes is limited in low-resource languages like Tamil due to the lack of simile datasets. This work introduces a manually annotated Tamil Simile Dataset (TSD) comprising around 1.5k simile sentences drawn from various sources. Our data annotation guidelines ensure that all the simile sentences are annotated with the three components, namely tenor, vehicle, and context. We benchmark our dataset for simile interpretation and simile generation tasks using chosen pre-trained language models (PLMs) and present the results. Our findings highlight the challenges of simile tasks in Tamil, suggesting areas for further improvement. We believe that TSD will drive progress in computational simile processing for Tamil and other low-resource languages, further advancing simile related tasks in Natural Language Processing.
Hydrangea@DravidianLanTech2025: Abusive language Identification from Tamil and Malayalam Text using Transformer Models
Authors: Shanmitha Thirumoorthy; Thenmozhi Durairaj; Ratnavel Rajalakshmi
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Abusive language toward women on the Internet has always been perceived as a danger to free speech and safe online spaces. In this paper, we discuss three transformer-based models - BERT, XLM-RoBERTa, and DistilBERT-in identifying gender-abusive comments in Tamil and Malayalam YouTube contents. We fine-tune and compare these models using a dataset provided by DravidianLangTech 2025 shared task for identifying the abusive content from social media. Compared to the models above, the results of XLM-RoBERTa are better and reached F1 scores of 0.7708 for Tamil and 0.6876 for Malayalam. BERT followed with scores of 0.7658 (Tamil) and 0.6671 (Malayalam). Of the DistilBERTs, performance was varyingly different for the different languages. A large difference in performance between the models, especially in the case of Malayalam, indicates that working in low-resource languages is difficult. The choice of a model is extremely critical in applying abusive language detection. The findings would be important information for effective content moderation systems in linguistically diverse contexts. In general, it would promote safe online spaces for women in South Indian language communities.
Towards Effective Emotion Analysis in Low-Resource Tamil Texts
Authors: Priyatharshan Balachandran; Uthayasanker Thayasivam; Randil Pushpananda; Ruvan Weerasinghe
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Emotion analysis plays a significant role in understanding human behavior and communication, yet research in Tamil language remains limited. This study focuses on building an emotion classifier for Tamil texts using machine learning (ML) and deep learning (DL), along with creating an emotion-annotated Tamil corpus for Ekman’s basic emotions. Our dataset combines publicly available data with re-annotation and translations. Along with traditional ML models we investigated the use of Transfer Learning (TL) with state-of-the-art models, such as BERT and Electra based models. Experiments were conducted on unbalanced and balanced datasets using data augmentation techniques. The results indicate that MultinomialNaive Bayes (MNB) and Support Vector Machine (SVM) performed well with TF-IDF and BoW representations, while among Transfer Learning models, LaBSE achieved the highest accuracy (63% balanced, 69% unbalanced), followed by TamilBERT and IndicBERT.
NAYEL@DravidianLangTech-2025: Character N-gram and Machine Learning Coordination for Fake News Detection in Dravidian Languages
Authors: Hamada Nayel; Mohammed Aldawsari; Hosahalli Lakshmaiah Shashirekha
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This paper introduces the detailed description of the submitted model by the team NAYEL to Fake News Detection in Dravidian Languages shared task. The proposed model uses a simple character n-gram TF-IDF as a feature extraction approach integrated with an ensemble of various classical machine learning classification algorithms. While the simplicity of the proposed model structure, although it outperforms other complex structure models as the shared task results observed. The proposed model achieved a f1-score of 87.5% and secured the 5th rank.
AnalysisArchitects@DravidianLangTech 2025: BERT Based Approach For Detecting AI Generated Product Reviews In Dravidian Languages
Authors: Abirami Jayaraman; Aruna Devi Shanmugam; Dharunika Sasikumar; Bharathi B
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The shared task on Detecting AI-generated Product Reviews in Dravidian Languages is aimed at addressing the growing concern of AI-generated product reviews, specifically in Malayalam and Tamil. As AI tools become more advanced, the ability to distinguish between human-written and AI-generated content has become increasingly crucial, especially in the domain of online reviews where authenticity is essential for consumer decision-making. In our approach, we used the ALBERT, IndicBERT, and Support Vector Machine (SVM) models to classify the reviews. The results of our experiments demonstrate the effectiveness of our methods in detecting AI-generated content.
AnalysisArchitects@DravidianLangTech 2025: Machine Learning Approach to Political Multiclass Sentiment Analysis of Tamil
Authors: Abirami Jayaraman; Aruna Devi Shanmugam; Dharunika Sasikumar; Bharathi B
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Sentiment analysis is recognized as an important area in Natural Language Processing (NLP) that aims at understanding and classifying opinions or emotions in text. In the political field, public sentiment is analyzed to gain insight into opinions, address issues, and shape better policies. Social media platforms like Twitter (now X) are widely used to express thoughts and have become a valuable source of real-time political discussions. In this paper, the shared task of Political Multiclass Sentiment Analysis of Tamil tweets is examined, where the objective is to classify tweets into specific sentiment categories. The proposed approach is explained, which involves preprocessing Tamil text, extracting useful features, and applying machine learning and deep learning models for classification. The effectiveness of the methods is demonstrated through experimental results and the challenges encountered while working on the analysis of Tamil political sentiment are discussed.
TEAM_STRIKERS@DravidianLangTech2025: Misogyny Meme Detection in Tamil Using Multimodal Deep Learning
Authors: Kogilavani Shanmugavadivel; Malliga Subramanian; Mohamed Arsath H; Ramya K; Ragav R
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This study focuses on detecting misogynistic content in memes under the title Misogynistic. Meme Detection Using Multimodal Deep Learning. Through an analysis of both textual and visual components of memes, specifically in Tamil, the study seeks to detect misogynistic rhetoric directed towards women. Preprocessing and vectorizing text data using methods like TF-IDF, GloVe, Word2Vec, and transformer-based embeddings like BERT are all part of the textual analysis process. Deep learning models like ResNet and EfficientNet are used to extract significant image attributes for the visual component. To improve classification performance, these characteristics are then combined in a multimodal framework employing hybrid architectures such as CNN-LSTM, GRU-EfficientNet, and ResNet-BERT. The classification of memes as misogynistic or non-misogynistic is done using sophisticated machine learning and deep learning ap proaches. Model performance is evaluated using metrics like Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. This study shows how multimodal deep learning can effectively detect and counteract negative narratives about women in digital media by combining natural language processing with image classification.
KCRL@DravidianLangTech 2025: Multi-Pooling Feature Fusion with XLM-RoBERTa for Malayalam Fake News Detection and Classification
Authors: Fariha Haq; Md. Tanvir Ahammed Shawon; Md Ayon Mia; Golam Sarwar Md. Mursalin; Muhammad Ibrahim Khan
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The rapid spread of misinformation on social media platforms necessitates robust detection mechanisms, particularly for languages with limited computational resources. This paper presents our system for the DravidianLangTech 2025 shared task on Fake News Detection in Malayalam YouTube comments, addressing both binary and multiclass classification challenges. We propose a Multi-Pooling Feature Fusion (MPFF) architecture that leverages [CLS] + Mean + Max pooling strategy with transformer models. Our system demonstrates strong performance across both tasks, achieving a macro-averaged F1 score of 0.874, ranking 6th in binary classification, and 0.628, securing 1st position in multiclass classification. Experimental results show that our MPFF approach with XLM-RoBERTa significantly outperforms traditional machine learning and deep learning baselines, particularly excelling in the more challenging multiclass scenario. These findings highlight the effectiveness of our methodology in capturing nuanced linguistic features for fake news detection in Malayalam, contributing to the advancement of automated verification systems for Dravidian languages.
KCRL@DravidianLangTech 2025: Multi-View Feature Fusion with XLM-R for Tamil Political Sentiment Analysis
Authors: Md Ayon Mia; Fariha Haq; Md. Tanvir Ahammed Shawon; Golam Sarwar Md. Mursalin; Muhammad Ibrahim Khan
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Political discourse on social media platforms significantly influences public opinion, necessitating accurate sentiment analysis for understanding societal perspectives. This paper presents a system developed for the shared task of Political Multiclass Sentiment Analysis in Tamil tweets. The task aims to classify tweets into seven distinct sentiment categories: Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None of the above. We propose a Multi-View Feature Fusion (MVFF) architecture that leverages XLM-R with a CLS-Attention-Mean mechanism for sentiment classification. Our experimental results demonstrate the effectiveness of our approach, achieving a macro-average F1-score of 0.37 on the test set and securing the 2nd position in the shared task. Through comprehensive error analysis, we identify specific classification challenges and demonstrate how our model effectively navigates the linguistic complexities of Tamil political discourse while maintaining robust classification performance across multiple sentiment categories.
TensorTalk@DravidianLangTech 2025: Sentiment Analysis in Tamil and Tulu using Logistic Regression and SVM
Authors: K Anishka; Anne Jacika J
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Words are powerful; they shape thoughts that influence actions and reveal emotions. On social media, where billions of people share theiropinions daily. Comments are the key to understanding how users feel about a video, an image, or even an idea. But what happens when these comments are messy, riddled with code-mixed language, emojis, and informal text? The challenge becomes even greater when analyzing low-resource languages like Tamil and Tulu. To tackle this, TensorTalk deployed cutting-edge machine learning techniques such as Logistic regression for Tamil language and SVM for Tulu language , to breathe life into unstructured data. By balancing, cleaning, and processing comments, TensorTalk broke through barriers like transliteration and tokenization, unlocking the emotions buried in the language.
TeamVision@DravidianLangTech 2025: Detecting AI generated product reviews in Dravidian Languages
Authors: Shankari S R; Sarumathi P; Bharathi B
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Recent advancements in natural language processing (NLP) have enabled artificial intelligence (AI) models to generate product reviewsthat are indistinguishable from those written by humans. To address these concerns, this study proposes an effective AI detector model capable of differentiating between AI-generated and human-written product reviews. Our methodology incorporates various machine learning techniques, including Naive Bayes, Random Forest, Logistic Regression, SVM, and deep learning approaches based on the BERT architecture.Our findings reveal that BERT outperforms other models in detecting AI-generated content in both Tamil product reviews and Malayalam product reviews.
CIC-NLP@DravidianLangTech 2025: Fake News Detection in Dravidian Languages
Authors: Tewodros Achamaleh; Nida Hafeez; Mikiyas Mebraihtu; Fatima Uroosa; Grigori Sidorov
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Misinformation is a growing problem for technologycompanies and for society. Although there exists a large body of related work on identifying fake news in predominantlyresource languages, there is unfortunately a lack of such studies in low-resource languages (LRLs). Because corpora and annotated data are scarce in LRLs, the identification of false information remains at an exploratory stage. Fake news detection is critical in this digital era to avoid spreading misleading information. This research work presents an approach to Detect Fake News in Dravidian Languages. Our team CIC-NLP work primarily targets Task 1 which involves identifying whether a given social platform news is original or fake. For fake news detection (FND) problem, we used mBERT model and utilized the dataset that was provided by the organizers of the workshop. In this work, we describe our findings and the results of the proposed method. Our mBERT model achieved an F1 score of 0.853.
CoreFour_IIITK@DravidianLangTech 2025: Abusive Content Detection Against Women Using Machine Learning And Deep Learning Models
Authors: Varun Balaji S; Bojja Revanth Reddy; Vyshnavi Reddy Battula; Suraj Nagunuri; Balasubramanian Palani
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The rise in utilizing social media platforms increased user-generated content significantly, including negative comments about women in Tamil and Malayalam. While these platforms encourage communication and engagement, they also become a medium for the spread of abusive language, which poses challenges to maintaining a safe online environment for women. Prevention of usage of abusive content against women as much as possible is the main issue focused in the research. This research focuses on detecting abusive language against women in Tamil and Malayalam social media comments using computational models, such as Logistic regression model, Support vector machines (SVM) model, Random forest model, multilingual BERT model, XLM-Roberta model, and IndicBERT. These models were trained and tested on a specifically curated dataset containing labeled comments in both languages. Among all the approaches, IndicBERT achieved a highest macro F1-score of 0.75. The findings emphasize the significance of employing a combination of traditional and advanced computational techniques to address challenges in Abusive Content Detection (ACD) specific to regional languages.
The_Deathly_Hallows@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages
Authors: Kogilavani Shanmugavadivel; Malliga Subramanian; Vasantharan K; Prethish G A; Santhosh S
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The DravidianLangTech@NAACL 2025 shared task focused on multimodal hate speech detection in Tamil, Telugu, and Malayalam using social media text and audio. Our approach integrated advanced preprocessing, feature extraction, and deep learning models. For text, preprocessing steps included normalization, tokenization, stopword removal, and data augmentation. Feature extraction was performed using TF-IDF, Count Vectorizer, BERT-base-multilingual-cased, XLM-Roberta-Base, and XLM-Roberta-Large, with the latter achieving the best performance. The models attained training accuracies of 83% (Tamil), 88% (Telugu), and 85% (Malayalam). For audio, Mel Frequency Cepstral Coefficients (MFCCs) were extracted and enhanced with augmentation techniques such as noise addition, time-stretching, and pitch-shifting. A CNN-based model achieved training accuracies of 88% (Tamil), 88% (Telugu), and 93% (Malayalam). Macro F1 scores ranked Tamil 3rd (0.6438), Telugu 15th (0.1559), and Malayalam 12th (0.3016). Our study highlights the effectiveness of text-audio fusion in hate speech detection and underscores the importance of preprocessing, multimodal techniques, and feature augmentation in addressing hate speech on social media.
SSN_IT_NLP@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media
Authors: Maria Nancy C; Radha N; Swathika R
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The proliferation of social media platforms has resulted in increased instances of online abuse, particularly targeting marginalized groups such as women. This study focuses on the classification of abusive comments in Tamil and Malayalam, two Dravidian languages widely spoken in South India. Leveraging a multilingual BERT model, this paper provides an effective approach for detecting and categorizing abusive and non-abusive text. Using labeled datasets comprising social media comments, our model demonstrates its ability to identify targeted abuse with promising accuracy. This paper outlines the dataset preparation, model architecture, training methodology, and the evaluation of results, providing a foundation for combating online abuse in low-resource languages.The methodology is unique for its integration of multilingual BERT and weighted loss functions to address class imbalance, showcasing a pathway for effective abuse detection in other underrepresented languages. The BERT model achieved an F1-score of 0.6519 for Tamil and 0.6601 for Malayalam. The codefor this work is available on github Abusive-Text-targeting-women