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Este martes 24 de junio se presentan los resultados del proyecto AMOR en Aranjuez en el curso de verano de la URJC organizado por CETINIA titulado "Human-centred Artificial Intelligence: How to bypass the Turing Tra ...

Hoy 12/12/2024 se presenta el proyecto AMOR en el UNICO I+D Project Meet-up Madrid organizado por el proyecto ELADAIS con la participación de los proyectos UNICO CLOUD financiados en la UPM (ELADAIS, MAP 6G, RISC ...

The article "To Click It or Not to Click It: An Italian Dataset for Neutralising Clickbait Headlines" has been presented at the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024). The publication i ...

Canal GSI

El artículo A framework for fake review detection in online consumer electronics retailers, por Rodrigo Barbado, Oscar Araque y Carlos A. Iglesias has sido aceptado y publicado en la revista Information Processing & Management. Esta revista se encuentra indexada en JCR: (Q1, 3.444).

 

Abstract:The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82% F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test.

 

Referencia:

Oscar Araque, Ganggao Zhu, Carlos A. Iglesias, A semantic similarity-based perspective of affect lexicons for sentiment analysis, Knowledge-Based Systems, Volume 165, 2019, Pages 346-359, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2018.12.005. (http://www.sciencedirect.com/science/article/pii/S0950705118305926).

 
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