A multi-granularity data augmentation based fusion neural network model for short text sentiment analysis

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

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Short text sentiment analysis based on convolutional neural network

2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) ◽

10.1109/wimob.2018.8589127 ◽

2018 ◽

Cited By ~ 3

Author(s):

Weisen Li ◽

Zhiqing Li ◽

Xupeng Fang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sentiment Analysis ◽

Short Text ◽

Text Sentiment Analysis

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Emoticon-Aware Recurrent Neural Network Model for Chinese Sentiment Analysis

2018 9th International Conference on Awareness Science and Technology (iCAST) ◽

10.1109/icawst.2018.8517232 ◽

2018 ◽

Author(s):

Da Li ◽

Rafal Rzepka ◽

Michal Ptaszynski ◽

Kenji Araki

Keyword(s):

Neural Network ◽

Sentiment Analysis ◽

Network Model ◽

Recurrent Neural Network ◽

Neural Network Model

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A Comparison of Regularization Techniques in Deep Neural Networks

Symmetry ◽

10.3390/sym10110648 ◽

2018 ◽

Vol 10 (11) ◽

pp. 648 ◽

Cited By ~ 6

Author(s):

Ismoilov Nusrat ◽

Sung-Bong Jang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Data Augmentation ◽

Training Data ◽

Regularization Techniques ◽

Performance Results ◽

Normalization Scheme ◽

Significant Attention

Artificial neural networks (ANN) have attracted significant attention from researchers because many complex problems can be solved by training them. If enough data are provided during the training process, ANNs are capable of achieving good performance results. However, if training data are not enough, the predefined neural network model suffers from overfitting and underfitting problems. To solve these problems, several regularization techniques have been devised and widely applied to applications and data analysis. However, it is difficult for developers to choose the most suitable scheme for a developing application because there is no information regarding the performance of each scheme. This paper describes comparative research on regularization techniques by evaluating the training and validation errors in a deep neural network model, using a weather dataset. For comparisons, each algorithm was implemented using a recent neural network library of TensorFlow. The experiment results showed that an autoencoder had the worst performance among schemes. When the prediction accuracy was compared, data augmentation and the batch normalization scheme showed better performance than the others.

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