Deep Non-linear and Unbiased Deep Decisive Pooling Learning–Based Opinion Mining of Customer Review

Saraswathi, K.; Renukadevi, Thangavel

doi:10.1007/s12559-022-10089-1

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…The prediction model of gas well type based on 2D-CNN is mainly composed of two layers of convolution, two pools, and two full connecting layers. In front of the first layer of the two-dimensional convolution layer, there is also a full connection layer whose activation function is Relu, which can add nonlinearity to the deep learning network [21]. For the first convolution layer, the number of convolution kernels is 4, the size of convolution kernels is 2 × 2, and the step size is 2 × 2.…”

Section: Parameter Setting and Prediction Process Of 2d-cnn Modelmentioning

confidence: 99%

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

Zhao,

Jia,

et al. 2024

Processes

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In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs.

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Section: Parameter Setting and Prediction Process Of 2d-cnn Modelmentioning

confidence: 99%

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

Zhao,

Jia,

et al. 2024

Processes

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“…Opinion mining, also known as sentiment analysis, is a field of data mining concerned with analyzing people's opinions about entities, such as services or products [1]. It is an important topic of interest and an active field of study in all languages, especially for languages that lack sufficient scientific research and resources, such as datasets, lexicons, corpora, and tools [2].…”

Section: Introductionmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

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Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.

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Mental Health on Twitter in Turkey: Sentiment Analysis with Transformers

Alshammari,

Akyüz

2024

Studies in Systems, Decision and Control

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Deep Non-linear and Unbiased Deep Decisive Pooling Learning–Based Opinion Mining of Customer Review

Cited by 9 publications

References 37 publications

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Mental Health on Twitter in Turkey: Sentiment Analysis with Transformers

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