Content Related Feature Analysis for Fake Online Consumer Review Detection

Vidanagama, Dushyanthi; Silva, Thushari; Karunananda, AS

doi:10.1007/978-981-16-0965-7_35

Cited by 4 publications

(1 citation statement)

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“…BERT is fine-tuned on a dataset of labeled reviews to learn how to distinguish between genuine and fake reviews by assigning higher weights to important words and phrases in the text. BERT's deep contextualized word representations are also useful for feature extraction in fake review detection, as they capture the meaning of a word in its context, allowing BERT to identify the most relevant parts of the text and to predict randomly hidden words for classification (Vidanagama et al 2021 ). This is especially helpful in detecting fake reviews, which often contain misleading or irrelevant information.…”

Section: Methodsmentioning

confidence: 99%

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Deshai

Rao

2023

Soft Comput

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Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.

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Section: Methodsmentioning

confidence: 99%