https://www.techscience.com/cmc/v70n2/44647/pdf

Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceives customers, inflicts losses, and tarnishes the reputation of companies. The present paper attempts to develop an intelligent system that can detect fake reviews on e-commerce platforms using n-grams of the review text and sentiment scores given by the reviewer. The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency (TF-IDF) approach for extracting features and their representation. For detection and classification, n-grams of review texts were inputted into the constructed models to be classified as fake or truthful. However, the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website. The classification results of these experiments showed that naïve Bayes (NB), support vector machine (SVM), adaptive boosting (AB), and random forest (RF) received 88%, 93%, 94%, and 95%, respectively, based on testing accuracy and tje F1-score. The obtained results were compared with existing works that used the same dataset, and the proposed methods outperformed the comparable methods in terms of accuracy. 

/pdf/data-analytics-for-the-identification-of-fake-reviews-using-13o3mio5.pdf

CLSA: A novel deep learning model for MOOC dropout prediction

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the...

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2020.1849057

An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19

Most consumers rely on online reviews when deciding to purchase e-commerce services or products. Unfortunately, the main problem of these reviews, which is not completely tackled, is the existence of deceptive reviews. The novelty of the proposed system is the application of opinion mining on consumers' reviews to help businesses and organizations continually improve their market strategies and obtain an in-depth analysis of the consumers' opinions regarding their products and brands. In this paper, the long short-term memory (LSTM) and deep learning convolutional neural network integrated with LSTM (CNN-LSTM) models were used for sentiment analysis of reviews in the e-commerce domain. The system was tested and evaluated by using real-time data that included reviews of cameras, laptops, mobile phones, tablets, televisions, and video surveillance products from the Amazon website. Data preprocessing steps, such as lowercase processing, stopword removal, punctuation removal, and tokenization, were used for data cleaning. The clean data were processed with the LSTM and CNN-LSTM models for the detection and classification of the consumers' sentiment into positive or negative. The LSTM and CNN-LSTM algorithms achieved an accuracy of 94% and 91%, respectively. We conclude that the deep learning techniques applied here provide optimal results for the classification of the customers' sentiment toward the products.

/pdf/developing-an-intelligent-system-with-deep-learning-15h4nfye.pdf

Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews

Recent years, many scientists address the research on text sentiment analysis of social media due to the exponential growth of social multimedia content. Natural language ambiguities and indirect sentiments within the social media text have made it hard to classify by using traditional machine learning approaches, such as support vector machines, naive Bayes, hybrid models and so on. This article aims to investigate the sentiment analysis of social media Chinese text by combining Bidirectional Long-Short Term Memory (BiLSTM) networks with a Multi-head Attention (MHAT) mechanism in order to overcome the deficiency of Sentiment Analysis that is performed with traditional machine learning. BiLSTM networks, not only solve the long-term dependency problem, but they also capture the actual context of the text. Due to the fact that the MHAT mechanism can learn the relevant information from a different representation subspace by using multiple distributed calculations, the purpose is to add influence weights to the constructed text sequence. The results of the numerical experiments show that the proposed model achieves better performance than the existing well-established methods.

Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention

The multi-stage degradation process of bearings significantly affects the predicted performance of rotating machinery and equipment in long-term operation. However, the inherent correlations between degradation stages, and domain-invariant degradation features of multistage that affect the remaining useful life (RUL) are mostly ignored, leading to RUL prognostics deterioration under cross-working conditions. Therefore, a novel deep transfer learning-based hierarchical adaptive RUL prediction approach is applied to overcome this problem. Firstly, a novel multistage degradation (MD) division method is proposed with a combination of maximum mean discrepancy and statistical process analysis to accurately obtain the varied health indicators (HIs) with MD without setting any threshold. Then, the obtained MD features are fed into the residual network to automatically recognize the HIs and MD in response to multi-variability trend changes. Finally, a novel hierarchical adaptive RUL model based on a deep adaptive-transformer is proposed to transfer the learned domain-invariant degradation features of multistage from one to various working conditions. Meanwhile, hierarchical adaptive tuning (HAT) reduces the RUL model loss to guarantee a more accurate prediction of each degradation stage. To validate the proposed approach, extensive experiments were conducted in seven cross-domain cases on the XJTU-SY dataset to predict RUL. Comparison results indicate that the proposed approach is more accurate than other conventional methods because it considers the RUL of bearing at each stage. 

Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation

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Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention

Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation