Improving Sentiment Analysis in Arabic Using Word Representation

The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text. In this paper, we describe how we construct Word2Vec models from a large Arabic corpus obtained from ten newspapers in different Arab countries. By applying different machine learning algorithms and convolutional neural networks with different text feature selections, we report improved accuracy of sentiment classification (91%-95%) on our publicly available Arabic language health sentiment dataset [1]Comment: Authors accepted version of submission for ASAR 201

A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.Comment: Authors accepted version of submission for CD-MAKE 201

Arabic Language Sentiment Analysis on Health Services

The social media network phenomenon leads to a massive amount of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.Comment: Authors accepted version of submission for ASAR 201

Leveraging Arabic sentiment classification using an enhanced CNN-LSTM approach and effective Arabic text preparation

The high variety in the forms of the Arabic words creates significant complexity related challenges in Natural Language Processing (NLP) tasks for Arabic text. These challenges can be dealt with by using different techniques for semantic representation, such as word embedding methods. In addition, approaches for reducing the diversity in Arabic morphologies can also be employed, for example using appropriate word normalisation for Arabic texts. Deep learning has proven to be very popular in solving different NLP tasks in recent years as well. This paper proposes an approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to improve sentiment classification, by excluding the max-pooling layer from the CNN. This layer reduces the length of generated feature vectors after convolving the filters on the input data. As such, the LSTM networks will receive well-captured vectors from the feature maps. In addition, the paper investigated different effective approaches for preparing and representing the text features in order to increase the accuracy of Arabic sentiment classification.</p

Enhancing early detection of Alzheimer’s disease through hybrid models based on feature fusion of multi-CNN and handcrafted features

Abstract Alzheimer’s disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer’s are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis. In this study, the images were enhanced and the active contour algorithm (ACA) was used to extract regions of interest (ROI) such as soft tissue and white matter. Strategies have been developed to diagnose AD and differentiate its stages. The first strategy is using XGBoost and ANN networks with the features of MobileNet, DenseNet, and GoogLeNet models. The second strategy is by XGBoost and ANN networks with combined features of MobileNet-DenseNet121, DenseNet121-GoogLeNet and MobileNet-GoogLeNet. The third strategy combines XGBoost and ANN networks with combined features of MobileNet-DenseNet121-Handcrafted, DenseNet121-GoogLeNet-Handcrafted, and MobileNet-GoogLeNet-Handcrafted leading to improved accuracy of the strategies and improved efficiency. XGBoost with hybrid features of DenseNet-GoogLeNet-Handcrafted achieved an AUC of 98.82%, accuracy of 98.8%, sensitivity of 98.9%, accuracy of 97.08%, and specificity of 99.5%

Utilising Acknowledge for the Trust in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are emerging networks that are being utilized in a variety of applications, such as remote sensing images, military, healthcare, and traffic monitoring. Those critical applications require different levels of security; however, due to the limitation of the sensor networks, security is a challenge where traditional algorithms cannot be used. In addition, sensor networks are considered as the core of the Internet of Things (IoT) and smart cities, where security became one of the most significant problems with IoT and smart cities applications. Therefore, this paper proposes a novel and light trust algorithm to satisfy the security requirements of WSNs. It considers sensor nodes’ limitations and cross-layer information for efficient secure routing in WSNs. It proposes a Tow-ACKs Trust (TAT) Routing protocol for secure routing in WSNs. TAT computes the trust values based on direct and indirect observation of the nodes. TAT uses the first-hand and second-hand information from the Data Link and the Transmission Control Protocol layers to modify the trust’s value. The suggested TATs’ protocols performance is compared to BTRM and Peertrust models in terms of malicious detection ratio, accuracy, average path length, and average energy consumption. The proposed algorithm is compared to BTRM and Peertrust models, the most recent algorithms that proved their efficiency in WSNs. The simulation results indicate that TAT is scalable and provides excellent performance over both BTRM and Peertrust models, even when the number of malicious nodes is high.</jats:p

Utilising Acknowledge for the Trust in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are emerging networks that are being utilized in a variety of applications, such as remote sensing images, military, healthcare, and traffic monitoring. Those critical applications require different levels of security; however, due to the limitation of the sensor networks, security is a challenge where traditional algorithms cannot be used. In addition, sensor networks are considered as the core of the Internet of Things (IoT) and smart cities, where security became one of the most significant problems with IoT and smart cities applications. Therefore, this paper proposes a novel and light trust algorithm to satisfy the security requirements of WSNs. It considers sensor nodes&rsquo; limitations and cross-layer information for efficient secure routing in WSNs. It proposes a Tow-ACKs Trust (TAT) Routing protocol for secure routing in WSNs. TAT computes the trust values based on direct and indirect observation of the nodes. TAT uses the first-hand and second-hand information from the Data Link and the Transmission Control Protocol layers to modify the trust&rsquo;s value. The suggested TATs&rsquo; protocols performance is compared to BTRM and Peertrust models in terms of malicious detection ratio, accuracy, average path length, and average energy consumption. The proposed algorithm is compared to BTRM and Peertrust models, the most recent algorithms that proved their efficiency in WSNs. The simulation results indicate that TAT is scalable and provides excellent performance over both BTRM and Peertrust models, even when the number of malicious nodes is high