In this new digital era, accessibility to real-world events is moving towards
web-based modules. This is mostly visible on e-commerce websites where there is
limited availability of physical verification. With this unforeseen
development, we depend on the verification in the virtual world to influence
our decisions. One of the decision making process is deeply based on review
reading. Reviews play an important part in this transactional process. And
seeking a real review can be very tenuous work for the user. On the other hand,
fake review heavily impacts these transaction records of a product. The article
presents an implementation of a Siamese network for detecting fake reviews. The
fake reviews dataset, consisting of 40K reviews, preprocessed with different
techniques. The cleaned data is passed through embeddings generated by MiniLM
BERT for contextual relationship and Word2Vec for semantic relationship to form
vectors. Further, the embeddings are trained in a Siamese network with LSTM
layers connected to fuzzy logic for decision-making. The results show that fake
reviews can be detected with high accuracy on a siamese network for prediction
and verification.
Analysis of Siamese Network for Detecting Fake Reviews
In today’s digital world, where accessibility to physical verification is often limited, we rely heavily on virtual platforms and online reviews to make informed decisions. However, the presence of fake reviews poses a significant challenge in this transactional process. Identifying genuine reviews from fake ones is crucial for users to make reliable choices.
The article presents an implementation of a Siamese network, a deep learning model known for its effectiveness in measuring similarities between inputs, for detecting fake reviews. The dataset used consists of 40,000 reviews, which have been preprocessed using various techniques to clean the data and make it suitable for analysis.
In order to capture the contextual and semantic relationships within the reviews, the cleaned data is transformed into numerical vectors using embeddings generated by MiniLM BERT and Word2Vec models. These embeddings capture the essence of the text and enable more meaningful comparisons between reviews.
The Siamese network architecture, with its LSTM layers, is then trained using the generated embeddings. The network is designed to extract relevant features from the review vectors and make predictions on whether a review is genuine or fake. The decision-making process is further enhanced by incorporating fuzzy logic, which allows for more nuanced analysis and decision rules based on the network’s outputs.
The results of the implementation demonstrate that fake reviews can be accurately detected with high precision using a Siamese network. This approach leverages the power of deep learning, natural language processing, and fuzzy logic to uncover patterns and anomalies in review data that are difficult to discern manually.
From a multidisciplinary perspective, this implementation highlights the seamless integration of concepts from various fields such as information retrieval, natural language processing, and artificial intelligence. The use of embeddings generated by MiniLM BERT and Word2Vec models showcases the importance of natural language understanding in deciphering the contextual and semantic relationships within textual data. The incorporation of fuzzy logic further emphasizes the role of computational intelligence in decision-making processes.
In the broader field of multimedia information systems, this implementation aligns with the growing demand for reliable and trustworthy information in the digital era. By leveraging advanced technologies like deep learning and artificial intelligence, it contributes to enhancing the quality and credibility of online platforms by detecting and filtering out fake reviews.
Moreover, this approach can also be applied in the domains of animations, artificial reality, augmented reality, and virtual realities, where user-generated content plays a crucial role. By automating the detection of fake reviews, content creators and platform providers can maintain the integrity and authenticity of their digital environments.
In conclusion, the implementation of a Siamese network for detecting fake reviews showcases the power of deep learning and multidisciplinary approaches in addressing real-world challenges. As technology continues to advance, such solutions will play a significant role in ensuring the reliability and transparency of digital transactions and interactions.