Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Imran, Mubashir; Muna, Jannatun Naeem; Billah, Md. Masum; Das, Protiva; Rahman, Osmud

doi:10.11591/ijeecs.v23.i1.pp463-470

Cited by 82 publications

(38 citation statements)

References 20 publications

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“…It is observed that the decision algorithm has the highest precision, recall and f1-score as well with 92%, 99% and 95.3% respectively. [22] 2019 ILPD LG 72.50 % Thaiparnit et al [23] 2018 Liver Disorder RF 75.76 % Rahman et al [24] 2019 ILPD LG 75% Kumar and Thakur [25] 2020 BUPA, ILPD Fuzzy-NWKNN 78.46% Rabbi et al [26] 2020 ILPD AdaBoost 92.19% Poonguzharselvi et al [27] 2021 UCI repository Random Forest 84%…”

Section: Resultsmentioning

confidence: 99%

Supervised machine learning based liver disease prediction approach with LASSO feature selection

et al. 2021

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In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.

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Section: Resultsmentioning

confidence: 99%

Supervised machine learning based liver disease prediction approach with LASSO feature selection

et al. 2021

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“…25,26 Another crucial problem is the selection of K value.A common work is to try the optimal K value continuously through cross-validation, started by selecting a smaller K value, increasing the value of K, then calculating the variance of the validation set, and finally finding a more appropriate K value. 27,28 The parameters used in the algorithm can be shown in table 2.…”

Section: K-nearest-neighbours Algorithmmentioning

confidence: 99%

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Ma,

Mustapha,

Ishak

et al. 2023

International Journal of Aeroacoustics

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Multi-rotor Unmanned Aerial Vehicles (UAVs) have become increasingly important in industries and early detection of structural damage is crucial to prevent unexpected breakdowns, ensure production efficiency, and maintain operational safety. This paper proposes machine learning techniques for detecting damage caused by loosened screws which is not easy founded based on vibration signals. An independent data acquisition device with a Micro Electro Mechanical Systems (MEMS) sensor is designed and fixed onto the multi-rotor UAVs to acquire the vibration data. Four machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, and Random Forest, are employed for damage detection. The results demonstrate successful utilization of the vibration data from the MEMS sensor for damage detection, with the random forest model outperforming other models with an accuracy of 90.07.

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“…Yet, the effectiveness of these approaches was constrained by their inability to grasp intricate language nuances and variations, prompting the exploration of more advanced techniques. Subsequent research in sentiment analysis witnessed a shift towards machine learning-based approaches, where supervised learning algorithms were utilized to categorize text into positive, negative, and neutral classes [11]. These methods demonstrated improved performance compared to rule-based techniques; however, they still faced limitations in effectively analyzing more intricate linguistic structures.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Deep Learning Approach for Real-Time Sentiment Analysis in Video Streaming

2023

IJACSA

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Recognizing emotions from visual data, like images and videos, presents a daunting challenge due to the intricacy of visual information and the subjective nature of human emotions. Over the years, deep learning has showcased remarkable success in diverse computer vision tasks, including sentiment classification. This paper introduces a novel multi-view deep learning framework for emotion recognition from visual data. Leveraging Convolutional Neural Networks (CNNs) this framework extracts features from visual data to enhance sentiment classification accuracy. Additionally, we enhance the deep learning model through cutting-edge techniques like transfer learning to bolster its generalization capabilities. Furthermore, we develop an efficient deep learning classification algorithm, effectively categorizing visual sentiments based on the extracted features. To assess its performance, we compare our proposed model with state-of-the-art machine learning methods in terms of classification accuracy, training time, and processing speed. The experimental results unequivocally demonstrate the superiority of our framework, showcasing higher classification accuracy, faster training times, and improved processing speed compared to existing methods. This multi-view deep learning approach marks a significant stride in emotion recognition from visual data and holds the potential for various real-world applications, such as social media sentiment analysis and automated video content analysis.

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Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Cited by 82 publications

References 20 publications

Supervised machine learning based liver disease prediction approach with LASSO feature selection

Supervised machine learning based liver disease prediction approach with LASSO feature selection

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Multimodal Deep Learning Approach for Real-Time Sentiment Analysis in Video Streaming

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