FRD-LSTM: a novel technique for fake reviews detection using DCWR with the Bi-LSTM method

Qayyum, Huma; Farooq, Ali; Nawaz, Marriam; Nazir, Tahira

doi:10.1007/s11042-023-15098-2

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…However, the model proposed in the present study has a lower performance when compared with the FRDLSTM model of Qayyum et al [26]. The FRDLSTM model applied the DCWR algorithm to compute deep features, used PCA to reduce the feature space, and identified fake reviews by training the Bi-LSTM model, which presents an interesting challenge for future work.…”

Section: Discussionmentioning

confidence: 79%

“…The research revealed that the highest level of practical efficiency was attained by integrating all three algorithms, leading to a notable accuracy rate of 95%. Qayyum et al [26] produced the FRD-LSTM, a deep learning-based method for detecting fake reviews, by using the DCWR algorithm to compute deep features and PCA to reduce the feature space. Training the Bi-LSTM helps to detect fake reviews.…”

Section: Related Workmentioning

confidence: 99%

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A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce

Puttarattanamanee,

Boongasame,

Thammarak

2023

HighTech. Innov. J.

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The popularity of the e-commerce system has increased, especially under the COVID scenario. Consumer product reviews from the past have had a significant impact on influencing consumers' purchasing decisions. Fake reviews—those written by humans and computers that engage in dishonest behavior—are consequently generated to increase product sales. The fake reviews hurt consumers and are dishonest. The goal of this research is to examine and evaluate the performance of various methods for identifying fake reviews. The well-known and widely-used Amazon Review Data (2018) dataset was used for this research. The first 10 product categories on Amazon.com with favorable feedback will be provided in the data section. After that, perform fundamental data preparation procedures such as special character trimming, bag of words, TF-IDF, etc. The models are trained to create a dataset for detecting fake reviews. This research compares the performance of four different models: GPT-2, NBSVM, BiLSTM, and RoBERTa. The hyperparameters of the models are also tuned to find the optimal values. The research concludes that the RoBERTa model performs the best overall, with an accuracy of 97%. GPT-2 has an overall accuracy of 82%, NBSVM has an overall accuracy of 95%, and BiLSTM has an overall accuracy of 92%. The research also calculates the Area Under the Curve (AUC) for each model and finds that RoBERTa has an AUC of 0.9976, NBSVM has an AUC of 0.9888, BiLSTM has an AUC of 0.9753, and GPT-2 has an AUC of 0.9226. It can be observed that the RoBERTa model has the highest AUC value, which is close to 1. Therefore, it can be concluded that this model provides the most accurate prediction for detecting fake reviews, which is the main focus of this research. Doi: 10.28991/HIJ-2023-04-02-08 Full Text: PDF

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Section: Discussionmentioning

confidence: 79%

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce

Puttarattanamanee,

Boongasame,

Thammarak

2023

HighTech. Innov. J.

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“…These studies outperform the rule-based approach but the success of these models heavily depends on the selection of the features that are derived from the annotated corpus and are used in the training process. Deep learning (DL) based studies use different pre-trained word embedding techniques [21][22][23][24][25][26][27] to map the words in vectors using the language vocabulary to automatically extract meaningful relationships among words in the dataset. Due to limited vocabulary size, out-of-vocabulary words pose significant challenges for morphology-rich languages [28] like Urdu due to language complexities [29].…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for Named Entity Recognition in Urdu language

Anam,

Anwar,

Jamal

et al. 2024

PLoS ONE

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Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

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