Using XGBoost and Skip-Gram Model to Predict Online Review Popularity

Nguyen, Lien T.; Chung, Hao Hsuan; Tuliao, Kristine Velasquez; Lin, Tom M.Y.

doi:10.1177/2158244020983316

Cited by 13 publications

(3 citation statements)

References 82 publications

(125 reference statements)

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“…Due to the fast learning and efficient memory, this algorithm was chosen. This algorithm combines several weak predictors to create a robust classifier ( Swamynathan, 2017 ; Nguyen et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

et al. 2022

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The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri’s movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 ± 1.36% and 92.25 ± 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 ± 2.02, 98.32 ± 1.93, 98.32 ± 1.93, and 98.88 ± 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 ± 1.73 and 3.44 ± 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 ± 0.08 and 97.85 ± 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control.

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Section: Methodsmentioning

confidence: 99%

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

et al. 2022

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“…XB, which stands for extreme gradient boosting, is a kind of boosting algorithm that is recognized mostly for providing parallel tree boosting that is well used for solving data science problems accurately and efficiently in terms of speed as performance 55 . This algorithm is also widely used to predict the online review polarity 56 based on customer purchase decisions where the key features are extracted from the data based on ranking scores. The precision, recall, and F1‐score for the XB model on the training results were 100% for all, respectively, while the precision, recall, and F1‐scores for the testing data were 83.4%, 83.1%, and 83.1%, respectively (see Table 5).…”

Section: Performance Analysis Of ML Modelsmentioning

confidence: 99%

Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective

Rahman¹,

Khan²,

Sarker

et al. 2022

Engineering Reports

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Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state‐of‐the‐art technologies has been focused on during the COVID‐19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID‐19 pandemic from a cross‐country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1‐score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID‐19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

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“…In addition, the probability that a context word 𝑐 remains near the target word 𝑡 can be calculated as follows [9]:…”

Section: Skip N-gram Modelmentioning

confidence: 99%

Deep learning method for visual question answering in the digital radiology domain

Gaidamavičius¹,

Iešmantas²

2022

Math. models, eng.

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Computer vision applications in the medical field are widespread, and language processing models have gained more and more interest as well. However, these two different tasks often go separately: disease or pathology detection is often based purely on image models, while for example patient notes are treated only from the natural language processing perspective. However, there is an important task between: given a medical image, describe what is inside it – organs, modality, pathology, location, and stage of the pathology, etc. This type of area falls into the so-called VQA area – Visual Question Answering. In this work, we concentrate on blending deep features extracted from image and language models into a single representation. A new method of feature fusion is proposed and shown to be superior in terms of accuracy compared to summation and concatenation methods. For the radiology image dataset VQA-2019 Med [1], the new method achieves 84.8 % compared to 82.2 % for other considered feature fusion methods. In addition to increased accuracy, the proposed model does not become more difficult to train as the number of unknown parameters does not increase, as compared with the simple addition operation for fusing features.

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Using XGBoost and Skip-Gram Model to Predict Online Review Popularity

Cited by 13 publications

References 82 publications

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective

Deep learning method for visual question answering in the digital radiology domain

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