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The Psychological Effects of Digital Companies’ Employees during the Phase of COVID-19 Pandemic Extracted from Online Employee Reviews
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In: Sustainability; Volume 14; Issue 5; Pages: 2609 (2022)
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Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model
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In: Applied Sciences; Volume 12; Issue 2; Pages: 692 (2022)
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Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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Abstract:
The advancement of the Internet has changed people’s ways of expressing and sharing their views with the world. Moreover, user-generated content has become a primary guide for customer purchasing decisions. Therefore, motivated by commercial interest, some sellers have started manipulating Internet ratings by writing false positive reviews to encourage the sale of their goods and writing false negative reviews to discredit competitors. These reviews are generally referred to as deceptive reviews. Deceptive reviews mislead customers in purchasing goods that are inconsistent with online information and thus obstruct fair competition among businesses. To protect the right of consumers and sellers, an effective method is required to automate the detection of misleading reviews. Previously developed methods of translating text into feature vectors usually fail to interpret polysemous words, which leads to such functions being obstructed. By using dynamic feature vectors, the present study developed several misleading review-detection models for the Chinese language. The developed models were then compared with the standard detection-efficiency models. The deceptive reviews collected from various online forums in Taiwan by previous studies were used to test the models. The results showed that the models proposed in this study can achieve 0.92 in terms of precision, 0.91 in terms of recall, and 0.91 in terms of F1-score. The improvement rate of our proposal is higher than 20%. Accordingly, we prove that our proposal demonstrated improved performance in detecting misleading reviews, and the models based on dynamic feature vectors were capable of more accurately capturing semantic terms than the conventional models based on the static feature vectors, thereby enhancing effectiveness.
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Keyword:
BERT; deep learning; detection of deceptive reviews; language model; natural language processing
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URL: https://doi.org/10.3390/app12073338
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Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3656 (2022)
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Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3634 (2022)
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[Ressenya del llibre] Julià-Muné, Joan (2019): Un segle de lingüística catalana: de la Lletra de convit a la GCC (1901- 2002). Lleida: Edicions de la Universitat de Lleida, 256 p
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In: Estudis romànics, 2022, vol. 44, p. 493-495 ; Ressenyes publicades (D-FLC) (2022)
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Adaptive Kompetenzen von Kindern mit Down-Syndrom – ein Follow-up über zehn Jahre ... : Adaptive competences of children with Down syndrome - a ten-year follow-up ...
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Predicting emotional links between genre, plot, and reader response ...
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Adaptive Kompetenzen von Kindern mit Down-Syndrom – ein Follow-up über zehn Jahre ; Adaptive competences of children with Down syndrome - a ten-year follow-up
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In: Empirische Sonderpädagogik 13 (2021) 2, S. 100-109 (2021)
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School-based language and literacy interventions for multilingual children and adolescents: A systematic evidence map and an overview of reviews ...
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Sentiment analysis in Galaxy with IMDB movie review dataset ...
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Sentiment analysis in Galaxy with IMDB movie review dataset ...
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E se a marca me responde com emoji?: impacto das características da CMC na perceção e atitudes face à marca
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