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Research Article 


Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning

Pooja kumari.

Abstract
Suggestion mining is a relatively new area & is challenged by issues like the complexity in a task or manual formulation, the knowledge of sentence-level semantics, figurative sentences, handling long & complex words, context dependence, & also very imbalanced class distribution. Deep learning is an industry that can be highly competitive in machine learning. We use the Random Multimodel Deep Learning (RMDL) approach in this paper to address the problem of suggestion mining using the SemEval-2019 Task 9 data sets. Though its data sets are very imbalanced and unstructured, we have utilized SMOTE techniques to extract class imbalance problems. To solve the imbalanced dataset problem, SMOTE (synthetic Minority oversampling technique) is a widely used over-sampling tool. Experimental findings show that the advantages of SMOTE to manage complex data and imbalanced data set are superior to our current SMOTE-RMDL (SMO-RMDL) model of the existing research process.

Key words: Suggestion Mining, Deep learning, CNN, RNN, DNN, RMDL, SMOTE


 
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How to Cite this Article
Pubmed Style

Pooja kumari. Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. EEO. 2021; 20(5): 2702-2711. doi:10.17051/ilkonline.2021.05.293


Web Style

Pooja kumari. Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. http://ilkogretim-online.org//?mno=63488 [Access: April 09, 2021]. doi:10.17051/ilkonline.2021.05.293


AMA (American Medical Association) Style

Pooja kumari. Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. EEO. 2021; 20(5): 2702-2711. doi:10.17051/ilkonline.2021.05.293



Vancouver/ICMJE Style

Pooja kumari. Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. EEO. (2021), [cited April 09, 2021]; 20(5): 2702-2711. doi:10.17051/ilkonline.2021.05.293



Harvard Style

Pooja kumari (2021) Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. EEO, 20 (5), 2702-2711. doi:10.17051/ilkonline.2021.05.293



Turabian Style

Pooja kumari. 2021. Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. Elementary Education Online, 20 (5), 2702-2711. doi:10.17051/ilkonline.2021.05.293



Chicago Style

Pooja kumari. "Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning." Elementary Education Online 20 (2021), 2702-2711. doi:10.17051/ilkonline.2021.05.293



MLA (The Modern Language Association) Style

Pooja kumari. "Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning." Elementary Education Online 20.5 (2021), 2702-2711. Print. doi:10.17051/ilkonline.2021.05.293



APA (American Psychological Association) Style

Pooja kumari (2021) Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning. Elementary Education Online, 20 (5), 2702-2711. doi:10.17051/ilkonline.2021.05.293








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