@Article{jai.2024.049685,
AUTHOR = {Glody Muka, Patrick Mukala},
TITLE = {Leveraging Pre-Trained Word Embedding Models for Fake Review Identification},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {6},
YEAR = {2024},
NUMBER = {1},
PAGES = {211--223},
URL = {http://www.techscience.com/jai/v6n1/57576},
ISSN = {2579-003X},
ABSTRACT = {Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models to detect and filter these fake reviews. However, with the same NLP techniques, machine-generated fake reviews are increasing exponentially. This work explores a powerful text representation technique called <i>Embedding models</i> to combat the proliferation of fake reviews in online marketplaces. Indeed, these embedding structures can capture much more information from the data compared to other standard text representations. To do this, we tested our hypothesis in two different Recurrent Neural Network (RNN) architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), using fake review data from Amazon and TripAdvisor. Our experimental results show that our best-proposed model can distinguish between real and fake reviews with 91.44% accuracy. Furthermore, our results corroborate with the state-of-the-art research in this area and demonstrate some improvements over other approaches. Therefore, proper text representation improves the accuracy of fake review detection.},
DOI = {10.32604/jai.2024.049685}
}