Feature Enhancement Based Text Sentiment Classification using Deep Learning Model

被引：0
|
作者：
Janardhana, D. R. [1 ]
Vijay, C. P. [2 ]
Swamy, G. B. Janardhana [1 ]
Ganaraj, K. [1 ]
机构：
[1] Sahyadri Coll Engn & Management, Dept Informat Sci Engn, Managaluru, India
[2] Vidyavardhaka Coll Engn, Dept Informat Sci Engn, Mysore, Karnataka, India
来源：
PROCEEDINGS OF THE 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND SECURITY (ICCCS-2020) | 2020年
关键词：
Text sentiment; Convolutional Neural Networks (CNN); Recurrent Neural Networks (RNN); Long Short-term Memory (LSTM); Convolutional Recurrent Neural Networks (CRNN);
D O I：
10.1109/icccs49678.2020.9277109
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Text sentiment classification is a significant task in the recent years to understand the opinions and thoughts hidden in the text to enhance more productivity in e-commerce websites and also in the social media. Here we integrate deep learning models to analyze the text sentiments. In this paper, Convolutional Recurrent Neural Network (CRNN) method for text sentiment analysis is proposed. The proposed CRNN is a combination of different layers used to extract the features from the text dataset. During training CRNN is able to learn the features set of the text sentiment dataset. The performance of the proposed approach is evaluated on text sentiments of publically available movie review (MR) dataset. Results show that the proposed method outperforms the traditional deep learning techniques
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页数：6
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