Arabic Text Classification Using Convolutional Neural Network and Genetic Algorithms

Alsaleh, Deem; Marie-Sainte, Souad Larabi

doi:10.1109/access.2021.3091376

Cited by 38 publications

(11 citation statements)

References 31 publications

(46 reference statements)

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“…TextCNN [51]: This model extracts local features by convolution operation and dimensionality reduction by pooling operation, and is a commonly used deep learning algorithm for text classification.…”

Section: B Baseline Methodsmentioning

confidence: 99%

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Sun

Huo

2022

IEEE Access

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Short text classification is an important branch of Natural Language Processing. Although CNN and RNN have achieved satisfactory results in the text classification tasks, they are difficult to apply to the Chinese short text classification because of the data sparsity and the homophonic typos problems of them. To solve the above problems, word-level and Pinyin-level based Chinese short text classification model is constructed. Since homophones have the same Pinyin, the addition of Pinyin-level features can solve the homophonic typos problem. In addition, due to the introduction of more features, the data sparsity problem of short text can be solved. In order to fully extract the deep hidden features of the short text, a deep learning model based on BiLSTM, Attention and CNN is constructed, and the residual network is used to solve the gradient disappearance problem with the increase of network layers. Additionally, considering that the complex deep learning network structure will increase the text classification time, the Text Center is constructed. When there is a new text input, the text classification task can be quickly realized by calculating the Manhattan distance between the embedding vector of it and the vectors stored in the Text Center. The Accuracy, Precision, Recall and F1 of the proposed model on the simplifyweibo_4_moods dataset are 0.9713, 0.9627, 0.9765 and 0.9696 respectively, and those on the online_shopping_10_cats dataset are 0.9533, 0.9416, 0.9608 and 0.9511 respectively, which are better than that of the baseline method. In addition, the classification time of the proposed model on simplifyweibo_4_moods and online_shopping_10_cats is 0.0042 and 0.0033 respectively, which is far lower than that of the baseline method.

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Section: B Baseline Methodsmentioning

confidence: 99%

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Sun

Huo

2022

IEEE Access

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“…Many benefits of DL make it suitable to adapt it for new problems. Convolutional neural network is a DL approach with many benefits [ 45 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Yousaf

Anwar

Imtiaz

et al. 2022

Computational Intelligence and Neuroscience

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Softwares are involved in all aspects of healthcare, such as booking appointments to software systems that are used for treatment and care of patients. Many vendors and consultants develop high quality software healthcare systems such as hospital management systems, medical electronic systems, and middle-ware softwares in medical devices. Internet of Things (IoT) medical devices are gaining attention and facilitate the people with new technology. The health condition of the patients are monitored by the IoT devices using sensors, specifically brain diseases such as Alzheimer, Parkinson’s, and Traumatic brain injury. Embedded software is present in IoT medical devices and the complexity of software increases day-by-day with the increase in the number and complexity of bugs in the devices. Bugs present in IoT medical devices can have severe consequences such as inaccurate records, circulatory suffering, and death in some cases along with delay in handling patients. There is a need to predict the impact of bugs (severe or nonsevere), especially in case of IoT medical devices due to their critical nature. This research proposes a hybrid bug severity prediction model using convolution neural network (CNN) and Harris Hawk optimization (HHO) based on an optimized hyperparameter of CNN with HHO. The dataset is created, that consists of the bugs present in healthcare systems and IoT medical devices, which is used for evaluation of the proposed model. A preprocessing technique on textual dataset is applied along with a feature extraction technique for CNN embedding layer. In HHO, we define the hyperparameter values of “Batch Size, Learning Rate, Activation Function, Optimizer Parameters, and Kernel Initializers,” before training the model. Hybrid model CNN-HHO is applied, and a 10-fold cross validation is performed for evaluation. Results indicate an accuracy of 96.21% with the proposed model.

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“…When CNNs are applied to texts instead of images, one-dimensional layer is usually used to extract features because texts are considered sequential data. However, the main concept remains the same for both data types [41].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Al-Khazaleh

Alian

Biltawi³

et al. 2023

Int. J. Onl. Eng.

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The emergence of social media platforms, which contributed in activating the patterns of connection between individuals, leads to the availability of a huge amount of content such as text, images, and videos. Twitter is one of the most popular platforms of social media that encourage researchers to investigate people’s feelings and opinions among through sentiment analysis studies that elicited the interest of researchers in natural language processing field. Many techniques related to machine learning and deep learning models could be used to improve the efficiency and performance of sentiment analysis, especially in complex classification problems. In this paper, different models of long short-term memory recurrent neural network are used for the sentiment classification task. The input text was represented as vectors using Arabic pre-trained word embedding (Aravec). Experiments were conducted using different dimensions of Aravec on 15779 tweets about COVID-19 collected and labeled as positive and negative. The experimental results show an accuracy value of 98%.

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Arabic Text Classification Using Convolutional Neural Network and Genetic Algorithms

Cited by 38 publications

References 31 publications

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Word-Level and Pinyin-Level Based Chinese Short Text Classification

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

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