Convolutional Deep Belief Network Based Short Text Classification on Arabic Corpus

Abstract

With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users. 11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day. In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective. In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus. The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media. The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage. Besides, the DSOCDBN-STC model involves CDBN based classification model for Arabic short text. At last, the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method. To establish the enhanced performance of the DSOCDBN-STC model, a wide range of simulations have been performed. The simulation results confirmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.

Evaluating online health information utilisation and its psychosocial implications among breast cancer survivors: Qualitative explorations

“…15,[25][26][27] A considerable gap exists in understanding how Arabic-speaking breast cancer patients, part of the approximately 440 million Arabicspeaking population who utilise the Arabic internet, interact with digital resources throughout their cancer care. 28 Scrutinising these experiences could potentially augment the quality of breast cancer care, and contribute to the refinement of clinical practice guidelines. Only a handful of studies have delved into the online informationseeking behaviors of cancer survivors, exploring their attitudes and experiences towards online cancer-related information, the obstacles they encounter in their quest for pertinent information, and the content they prefer in online health resources.…”