A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews
The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model cons...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/5217491 |
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| _version_ | 1849401240016912384 |
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| author | Kifayat Ullah Anwar Rashad Muzammil Khan Yazeed Ghadi Hanan Aljuaid Zubair Nawaz |
| author_facet | Kifayat Ullah Anwar Rashad Muzammil Khan Yazeed Ghadi Hanan Aljuaid Zubair Nawaz |
| author_sort | Kifayat Ullah |
| collection | DOAJ |
| description | The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN (one-dimensional convolutional neural network) for extracting features. A global max-pooling layer is used to reduce dimensions. A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models. |
| format | Article |
| id | doaj-art-85e0ad63c1ea4c00ba2ef798e4aa5bce |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-85e0ad63c1ea4c00ba2ef798e4aa5bce2025-08-20T03:37:49ZengWileyComplexity1099-05262022-01-01202210.1155/2022/5217491A Deep Neural Network-Based Approach for Sentiment Analysis of Movie ReviewsKifayat Ullah0Anwar Rashad1Muzammil Khan2Yazeed Ghadi3Hanan Aljuaid4Zubair Nawaz5Department of Computer and Software TechnologyDepartment of Computer and Software TechnologyDepartment of Computer and Software TechnologyDepartment of Software Engineering/Computer ScienceComputer Sciences DepartmentDepartment of Data ScienceThe number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN (one-dimensional convolutional neural network) for extracting features. A global max-pooling layer is used to reduce dimensions. A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models.http://dx.doi.org/10.1155/2022/5217491 |
| spellingShingle | Kifayat Ullah Anwar Rashad Muzammil Khan Yazeed Ghadi Hanan Aljuaid Zubair Nawaz A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews Complexity |
| title | A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews |
| title_full | A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews |
| title_fullStr | A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews |
| title_full_unstemmed | A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews |
| title_short | A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews |
| title_sort | deep neural network based approach for sentiment analysis of movie reviews |
| url | http://dx.doi.org/10.1155/2022/5217491 |
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