Prediction and modelling online reviews helpfulness using 1D Convolutional Neural Networks

Olmedilla, M.; Torres, María del Rocío Martínez; Toral, S. L.

doi:10.1016/j.eswa.2022.116787

Cited by 25 publications

(4 citation statements)

References 53 publications

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“…The local connectivity, weight sharing, and pooling operation features of the CNN can reduce the number of training parameters and effectively reduce the complexity of the network while making the model invariant to translation, distortion, and scaling to a certain degree, improving robustness and fault tolerance. Based on these superior properties, it performs better than standard fully connected neural networks in a variety of signal and information processing tasks which have achieved good results in the fields of computer vision, natural language processing, medicine and health, and environmental protection (Bhatti et al, 2019;Olmedilla et al, 2022;Serna et al, 2022).…”

Section: Principle Of the Convolutional Neural Networkmentioning

confidence: 99%

Water chemical oxygen demand prediction model based on the CNN and ultraviolet-visible spectroscopy

Ye¹,

Cao²,

Liu³

et al. 2022

Front. Environ. Sci.

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Excessive levels of organic matter in water threaten ecological safety and endanger human health. As the water resource environment is deteriorating, accurate and rapid determination of water quality parameters has become a current research hotspot. In recent years, the ultraviolet spectrometry method has been more widely used in the detection of chemical oxygen demand (COD), which is convenient and without chemical reagents. However, this method tends to use absorbance at 254 nm to measure COD. It has a good detection effect when the composition of pollutants is single, but in real life, the complex composition of pollutants will seriously affect the accuracy of measurement. Therefore, a COD prediction model based on ultraviolet-visible (UV-Vis) spectrometry and the convolutional neural network (CNN) is proposed. Compared with other traditional COD prediction models, this model makes full use of the absorbance of all ultraviolet and visible wavelengths, avoiding the information loss caused by using specific wavelengths. Meanwhile, this model is constructed based on the shallow CNN, using convolutional layers with different step lengths instead of the traditional pooling layers, which reduces computation and enhances the capture of spectral feature peaks. Additionally, with the powerful feature extraction capability of the CNN, this model reduces the reliance on pre-processing methods and improves the utilization of spectral information. Experiments have shown that our model has better fitting results and accuracy than other traditional COD prediction models such as the principal component analysis (PCA), partial least squares regression (PLSR), and backpropagation (BP) neural network. This study provides a better solution for improving the accuracy of UV-Vis water quality COD detection, which is conducive to real-time monitoring of the water quality, providing data support of water pollution and its development trend for the government’s water resource protection policy and promoting biodiversity development.

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Section: Principle Of the Convolutional Neural Networkmentioning

confidence: 99%

Water chemical oxygen demand prediction model based on the CNN and ultraviolet-visible spectroscopy

Ye¹,

Cao²,

Liu³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

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“…Previous research used the helpful votes to represent the number of helpful votes [4], [6], [7]. Moreover, the categorized helpful vote form is also used as a target variable [3], [19], [33], [34].…”

Section: A Helpfulness Metricsmentioning

confidence: 99%

“…[33] F54 verified purchase [19] Amazon.com 2014 [2], [4], [5], [19], [32], JD.com [3], TripAdvisor [33], Ciao.co.uk [34], Drugs.com, Yelpf.com [5] 10-fold CV [3], Time-based [19], Total sampling [19], Simple random [19] M2 [4], M4 [5], [32], M8 [2], M9 [3], M11 [33], M11 logistic regression in XGB classifier [19] and CNN/cross entropy loss [19], [34] M12 Zero Inflated Poisson [31] This study helpful vote (R3 ) review text (F4 ) Amazon.com 2018 [22] and IMDb [23] Adjusted 10fold CV (ACV), adaptive window size (AWS) Distribution-adapted model in LM, XGB and CNN Researchers were motivated by the results in the helpfulness rating to continue to employ both models to estimate the number of helpful votes [4], [6], [7], [32] although the distribution is not normal. The central limit theory also supports this condition that a large dataset tends toward a normal distribution in many situations, even if the original variables themselves are not normally distributed [38].…”

Section: B Helpful Vote Prediction Modelsmentioning

confidence: 99%

Distribution-Adapted Model for Helpful Vote Prediction

Saptono

Mine

2022

IEEE Access

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The number of helpful votes on a review is an essential indicator of how much impact the review has on other customers in electronic commerce. Therefore, predicting the number of helpful votes is an important task. Regression analysis and Tobit modeling are typical methods of prediction. Those methods come from the same initial assumption that the number of helpful votes follows a normal distribution on any dataset. However, the assumption is not usually confirmed, and the distribution of the helpful votes often follows other distributions. This paper proposes a framework for investigating the feasibility of building a model that predicts the number of helpful votes according to the distribution of the number of helpful votes. On top of that, considering the review age, we propose an adaptive window size sampling method to evaluate the model on review datasets sorted chronologically. The experimental results validated that the model adapting to the best approximate distribution gives a significant improvement compared to the baseline models. In addition, model evaluation using the adaptive window size sampling method has significant impacts on the performance on large datasets.

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“…In addition, in terms of the perspective of market attributes, market demand analysis has gradually become a crucial step in assisting technological innovation. It can facilitate user purchase behavior and product or service improvement [49,56]. Therefore, this study integrates the market demand and technical features to consider this issue regarding EVs.…”

Section: Understand and Define The Problemmentioning

confidence: 99%

Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes

et al. 2022

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The vigorous development of electric vehicles (EVs) can promote the green and low-carbon development of society and the environment. However, the research and development of EVs technology in China started late, and there are some problems such as relatively backward technology. In order to promote the decarbonization process of transportation systems, there is an urgent need for appropriate methods to identify promising technologies in the EVs field to guide the efficient development of innovation activities. This study proposes a novel approach to integrate the perspective of market and technical attributes to identify promising EVs technologies. Firstly, text mining tools are applied to extract review and technical keywords from online reviews and patents, and technical topics are summarized. Secondly, sentiment analysis is conducted to calculate user satisfaction based on online reviews, and then market demand of technical topics is obtained. Thirdly, social network centrality analysis, DEA–Malmquist model, and CRITIC method are employed to obtain technical features of technical topics based on patents. Finally, a portfolio map is constructed to analyze technical topics and identify promising EVs technologies. As the main driving force for the development and transformation of the automotive industry, the efficient identification of promising technologies in this field can provide strategic decision support for the development of EVs. This study aims to provide objective data and scientific guidance for related enterprises to carry out technological innovation activities.

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Prediction and modelling online reviews helpfulness using 1D Convolutional Neural Networks

Cited by 25 publications

References 53 publications

Water chemical oxygen demand prediction model based on the CNN and ultraviolet-visible spectroscopy

Water chemical oxygen demand prediction model based on the CNN and ultraviolet-visible spectroscopy

Distribution-Adapted Model for Helpful Vote Prediction

Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes

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