@inproceedings{sathvik-etal-2025-team,
title = "Team-Risers@{D}ravidian{L}ang{T}ech 2025: {AI}-Generated Product Review Detection in {D}ravidian Languages Using Transformer-Based Embeddings",
author = "Sathvik, Sai and
Palli, Muralidhar and
NNL, Keerthana and
Palani, Balasubramanian and
Jose, Jobin and
Rajamanickam, Siranjeevi",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.7/",
doi = "10.18653/v1/2025.dravidianlangtech-1.7",
pages = "33--37",
ISBN = "979-8-89176-228-2",
abstract = "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 95.68{\textbackslash}{\%} on Tamil data and an accuracy of 88.75{\textbackslash}{\%} on Malayalam data."
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<abstract>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 95.68\textbackslash% on Tamil data and an accuracy of 88.75\textbackslash% on Malayalam data.</abstract>
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%0 Conference Proceedings
%T Team-Risers@DravidianLangTech 2025: AI-Generated Product Review Detection in Dravidian Languages Using Transformer-Based Embeddings
%A Sathvik, Sai
%A Palli, Muralidhar
%A NNL, Keerthana
%A Palani, Balasubramanian
%A Jose, Jobin
%A Rajamanickam, Siranjeevi
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F sathvik-etal-2025-team
%X 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 95.68\textbackslash% on Tamil data and an accuracy of 88.75\textbackslash% on Malayalam data.
%R 10.18653/v1/2025.dravidianlangtech-1.7
%U https://aclanthology.org/2025.dravidianlangtech-1.7/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.7
%P 33-37
Markdown (Informal)
[Team-Risers@DravidianLangTech 2025: AI-Generated Product Review Detection in Dravidian Languages Using Transformer-Based Embeddings](https://aclanthology.org/2025.dravidianlangtech-1.7/) (Sathvik et al., DravidianLangTech 2025)
ACL
- Sai Sathvik, Muralidhar Palli, Keerthana NNL, Balasubramanian Palani, Jobin Jose, and Siranjeevi Rajamanickam. 2025. Team-Risers@DravidianLangTech 2025: AI-Generated Product Review Detection in Dravidian Languages Using Transformer-Based Embeddings. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 33–37, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.