Deceptive reviews and sentiment polarity: Effective link by exploiting BERT-Reference-Cited by-同舟云学术

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Deceptive reviews and sentiment polarity: Effective link by exploiting BERT

Published:2022-12 Issue: Volume:209 Page:118290
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Catelli Rosario^ORCID,Fujita Hamido^ORCID,De Pietro Giuseppe^ORCID,Esposito Massimo^ORCID

Funder

Ministero dell’Istruzione, dell’Università e della Ricerca

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference68 articles.

1. Detecting deceptive reviews using generative adversarial networks;Aghakhani,2018

2. Contextual string embeddings for sequence labeling;Akbik,2018

3. The value of online customer reviews;Askalidis,2016

4. A theoretical framework to identify authentic online reviews;Banerjee;Online Information Review,2014

5. Using supervised learning to classify authentic and fake online reviews;Banerjee,2015

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2. Effective text classification using BERT, MTM LSTM, and DT;Data & Knowledge Engineering;2024-05

3. On Students’ Behavior Prediction for Library Service Quality Using Bidirectional Deep Machine Learning;Lecture Notes in Networks and Systems;2024

4. Predicting stock market by sentiment analysis and deep learning;Operations Research and Decisions;2024

5. An improved BERT method for the evolution of network public opinion of major infectious diseases: Case Study of COVID-19;Expert Systems with Applications;2023-12

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