Artículo
Prediction and modelling online reviews helpfulness using 1D Convolutional Neural Networks
Autor/es | Olmedilla, María
Martínez Torres, María del Rocío Toral, S. L. |
Departamento | Universidad de Sevilla. Departamento de Administración de Empresas y Comercialización e Investigación de Mercados (Marketing) Universidad de Sevilla. Departamento de Ingeniería Electrónica |
Fecha de publicación | 2022-07 |
Fecha de depósito | 2024-01-29 |
Publicado en |
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Resumen | The latest research shows that the identification of helpful reviews from a large volume of user–generated data is a trending topic. The present study uses an approach that not only predicts if an online review is helpful, ... The latest research shows that the identification of helpful reviews from a large volume of user–generated data is a trending topic. The present study uses an approach that not only predicts if an online review is helpful, neutral or not helpful with 66% accuracy, but most importantly models online review helpfulness. To this end, we use an adaptive implementation of 1D Convolutional Neural Networks (CNNs). The neuronal encoding of CNNs has the benefit of obtaining automatic data classification using cluster analysis to detect different types of clusters of helpful and not helpful reviews, categorized by their most important contextual characteristics. Findings reveal that the clusters with the most important words and documents for helpful reviews in the product category ‘Cars & Motorcycles’ describe cars and their characteristics, whereas not helpful reviews concern details about car-related shops/companies in general. By demonstrating high performance on prediction and classification of review helpfulness with our proposed methodology, we are contributing to the research on business intelligence. In addition, we provide significant practical implications for marketers, enabling them to distinguish between helpful and not helpful reviews. Using the resulting encoding can produce automatic data classification of different clusters of specific topics. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | PID2020-114527RB-I00
10.13039/501100011033 PY20_00639 |
Cita | Olmedilla, M., Martínez Torres, M.d.R. y Toral, S.L. (2022). Prediction and modelling online reviews helpfulness using 1D Convolutional Neural Networks. Expert Systems with Applications, 198, 116787. https://doi.org/10.1016/j.eswa.2022.116787. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Prediction_and_modeling_online.pdf | 1.644Mb | [PDF] | Ver/ | Versión aceptada |