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Genuine Online Reviewer Identification using ML Techniques

Chengalakkattu Ajayan, Gayathri (2023) Genuine Online Reviewer Identification using ML Techniques. Masters thesis, Dublin, National College of Ireland.

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Customers are constantly trusting digital reviews as a form of insight before taking any purchase choice. Authentic item evaluations generally get a big consequence upon future product orders. Under this method, we put forth the effort in analysing costumers’ feedback that has been posted in effort to comprehend the aspects of their actions. In contrast side, finding bogus comments and filtering these from the dataset employing multiple Natural Language Processing (NLP) procedures becomes essential in a wide range of contexts. The hybrid Convolution Neural Network-Long Short Term Memory (CNN-LSTM) Machine Learning (ML) system is applied to review database to train it to forecast how a comment is positive or negative utilizing the sentiment analysis (SA) approach. On the basis of the Crowd Sourcing concept’s consolidated result, the Genuine Reviewer is determined. Prior to actual purchase, the customer reliance on item ratings in the e-commerce sector and possibly on different forums is growing. Therefore, the situation of false reviews should be handled so those multiple firm like eBay, Amazon etc could resolve it while also getting rid of the fraudsters and bogus critics, preserving customers’ faith in websites and other internet portals. To evaluate the chosen model, accuracy, recall, precision and F1 Score are employed. The accuracy for the Baseline model is 97 percent.

Item Type: Thesis (Masters)
Basilio, Jorge
Uncontrolled Keywords: Natural Language Processing (NLP); Genuine Reviewer; sentimental analysis (SA); Crowd Sourcing; Machine Learning (ML)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HF Commerce > Electronic Commerce
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 May 2023 14:11
Last Modified: 17 May 2023 14:11

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