@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://preview.aclanthology.org/fix-sig-urls/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."
}