Deceptive opinion spam detection using bidirectional long short-term memory with capsule neural network

Shinde, Sandeep A.; Pawar, Ranjeet R.; Jagtap, Asmita A.; Tambewagh, Pratibha A.; Rajput, Punam U.; Mali, Mohan K.; Kale, Satish D.; Mulik, Sameer V.

doi:10.1007/s11042-023-17348-9

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…In this work, three well-known deep learning techniques-Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs)-are applied using a robust strategy, drawing on insights from the abovementioned related works. This multipronged approach seeks to attain maximum precision in tackling the issues presented by fraudulent opinion spam and phony customer evaluations [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Truthful and Deceptive Hotel Reviews

Gupta,

Bhargava,

Agarwal

et al. 2024

Sustainability

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For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Truthful and Deceptive Hotel Reviews

Gupta,

Bhargava,

Agarwal

et al. 2024

Sustainability

View full text Add to dashboard Cite

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MBO-DeBERTa : High Performance Fake Review Detection using pretrained DeBERTa Optimized with Monarch Butterfly paradigm

et al. 2024

Preprint

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In this era of internet, e-commerce has grown tremendously and the customers are increasingly relying on reviews for product information. As these reviews influence the purchasing ability of the future customer, it can give a positive or negative impact on the businesses. The effectiveness of online reviews is compromised by fake reviews that provide false information about the product. Fake reviews can not only impact the reputation of the businesses but also involve financial losses. Thus, detection of fake reviews is essential to solve the problem for maintaining the integrity of online reviews. Existing Machine learning models often struggle with deep contextual understanding. Scaling machine learning models while maintaining accuracy and efficiency becomes increasingly challenging as the volume of online reviews continues to grow. Hence, this research work introduces a novel MBO-DeBERTa ,a deep neural network with Monarch Butterfly Optimizer. The proposed model improves the capacity to differentiate between overlapping characteristics of fake and authentic reviews. MBO-DeBERTa attained a classification accuracy of 98% for detecting the fake reviews. The proposed framework is tested on three different datasets such as Amazon, Fake Review and Deceptive Opinion Spam containing 21000,40000 and 1600 reviews respectively which are publicly available in Kaggle. The results show that the suggested model outperforms the current models showing increased accuracy, precision, recall, F1 score and reduced loss rate.

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Deceptive opinion spam detection using bidirectional long short-term memory with capsule neural network

Cited by 2 publications

References 33 publications

Deep Learning-Based Truthful and Deceptive Hotel Reviews

Deep Learning-Based Truthful and Deceptive Hotel Reviews

MBO-DeBERTa : High Performance Fake Review Detection using pretrained DeBERTa Optimized with Monarch Butterfly paradigm

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