Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text’s actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews.

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations. The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs. Hence, many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks. In this work, we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress. We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them. After reviewing the representative works for each dimension, we discuss promising future research directions in this rapidly growing field.

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combination term weighting scheme, namely, CmTLB. We utilize the mTFIDF that takes into account the missing terms and the number of the documents in which the term appears when calculating the term weight. For further robust term weight, we combine mTFIDF with two weighting methods. We evaluate CmTLB against the studied weighting schemes by the quality of the learned topics (topic visualization and topic coherence), classification, and clustering tasks. We applied weighting schemes to Latent Dirichlet allocation (LDA) and Dirichlet multinomial mixture (DMM) on eight Arabic long and short document datasets, respectively. The experiment results outline that appropriate weighting schemes can effectively improve topic modeling performance on Arabic texts. More importantly, our proposed CmTLB significantly outperforms the other weighting schemes. Secondly, we investigate whether the Arabic stemming process can improve topic modeling performance. We study the three approaches of Arabic stemming including root-based, stem-based, and statistical approaches. We also train topic models with weighting schemes on documents after applying four stemmers related to different stemming approaches. The results outline that applying the stemming process not only reduces the dimensionality of term-document matrix leading to fast estimation process, but also show enhancement of topic modeling performance both on short and long Arabic documents. Moreover, Farasa stemmer achieves the highest performance in most cases, since it prevents the ambiguity that may happen because of the blind removal of the affixes such as in root-based or stem-based stemmers.

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The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in real-time. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.