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A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification
Deep learning became more popular in recent years. It is widely used for different machine learning tasks. One such task is sentiment prediction on a text document. Fine-grained sentiment analysis is highly recommended since most of the researchers are focusing on binary sentiment classification. In...
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Published in: | International Journal on Electrical Engineering and Informatics 2021-06, Vol.13 (2), p.465-476 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | Deep learning became more popular in recent years. It is widely used for different machine learning tasks. One such task is sentiment prediction on a text document. Fine-grained sentiment analysis is highly recommended since most of the researchers are focusing on binary sentiment classification. In this work, a new model which combines the benefits of both Convolutional Neural Network (CNN) and Random Forest (RF) Classifier is proposed for finegrained sentiment classification. The main idea of the proposed model is to achieve maximum accuracy for sentiment classification on large volume of data. The CBOW (Continuous Bag-of-Words) model is used for converting the text input into vector form. Convolutional Neural Network (CNN) is used to extract the features from the input vector. The fully connected layer in the Convolutional Neural Network is replaced by the Random Forest classifier. Then the extracted features are used for the classification process by Random Forest Classifier. A dropout strategy is applied to regularize the CNNRF model to avoid overfitting. Sentiment analysis is performed on product review data by using CNN and RF model separately. The result of the CNN model and RF model is compared with the result of the proposed CNNRF model. The experiment result shows that the combined CNNRF model gave high performance than independent CNN and RF models. |
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ISSN: | 2085-6830 2087-5886 |
DOI: | 10.15676/ijeei.2020.13.2.13 |