@inproceedings{melleng-etal-2023-data,
    title = "Data Fusion for Better Fake Reviews Detection",
    author = "Melleng, Alimuddin  and
      Jurek-Loughrey, Anna  and
      P, Deepak",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.ranlp-1.79/",
    pages = "730--738",
    abstract = "Online reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings. In this paper, we first explore the effectiveness of different data representations, including emotion, document embedding, n-grams, and noun phrases in embedding for mat, for fake reviews detection. We evaluate these representations with various state-of-the-art deep learning models, such as BILSTM, LSTM, GRU, CNN, and MLP. Following this, we propose to incorporate different data repre- sentations and classification models using early and late data fusion techniques in order to im- prove the prediction performance. The experiments are conducted on four datasets: Hotel, Restaurant, Amazon, and Yelp. The results demonstrate that combination of different data representations significantly outperform any of the single data representations"
}