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Sentiment Classification Using Convolutional Neural Networks

Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(11), 2347;
Received: 29 April 2019 / Revised: 22 May 2019 / Accepted: 4 June 2019 / Published: 7 June 2019
(This article belongs to the Special Issue Sentiment Analysis for Social Media)
As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models. View Full-Text
Keywords: deep learning; convolutional neural network; sentiment classification deep learning; convolutional neural network; sentiment classification
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MDPI and ACS Style

Kim, H.; Jeong, Y.-S. Sentiment Classification Using Convolutional Neural Networks. Appl. Sci. 2019, 9, 2347.

AMA Style

Kim H, Jeong Y-S. Sentiment Classification Using Convolutional Neural Networks. Applied Sciences. 2019; 9(11):2347.

Chicago/Turabian Style

Kim, Hannah; Jeong, Young-Seob. 2019. "Sentiment Classification Using Convolutional Neural Networks" Appl. Sci. 9, no. 11: 2347.

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