A powerful comparison of deep learning frameworks for Arabic sentiment analysis

Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain

Different valuable tools for Arabic sentiment analysis: a comparative evaluation

Arabic Natural language processing (ANLP) is a subfield of artificial intelligence (AI) that tries to build various applications in the Arabic language like Arabic sentiment analysis (ASA) that is the operation of classifying the feelings and emotions expressed for defining the attitude of the writer (neutral, negative or positive). In order to work on ASA, researchers can use various tools in their research projects without explaining the cause behind this use, or they choose a set of libraries according to their knowledge about a specific programming language. Because of their libraries' abundance in the ANLP field, especially in ASA, we are relying on JAVA and Python programming languages in our research work. This paper relies on making an in-depth comparative evaluation of different valuable Python and Java libraries to deduce the most useful ones in Arabic sentiment analysis (ASA). According to a large variety of great and influential works in the domain of ASA, we deduce that the NLTK, Gensim and TextBlob libraries are the most useful for Python ASA task. In connection with Java ASA libraries, we conclude that Weka and CoreNLP tools are the most used, and they have great results in this research domain

A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document

Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset

Sustainable reuse of coal mine waste : experimental and economic assessments for embankments and pavement layer applications in Morocco

This paper examines the potential reuse of coal mine waste rocks (CMWR) as an alternative material for road construction to conserve the natural resources and sustainable management of mining waste. The investigation was conducted through the determination of the chemical, mineralogical, geotechnical properties, and acid mine drainage formulation of CMWR as well as economic feasibility. An economic case study confirmed the workability of CMWR reuse in a radius of 29 km around their dumps. Results confirmed that weathered CMWR can be successfully used as a sustainable alternative material for creating embankments

Non-genomic Effects of Glucocorticoids: An Updated View

Glucocorticoid (GC) anti-inflammatory effects generally require a prolonged onset of action and involve genomic processes. Because of the rapidity of some of the GC effects, however, the concept that non-genomic actions may contribute to GC mechanisms of action has arisen. While the mechanisms have not been completely elucidated, the non-genomic effects may play a role in the management of inflammatory diseases. For instance, we recently reported that GCs ‘rapidly’ enhanced the effects of bronchodilators, agents used in the treatment of allergic asthma. In this review article, we discuss (i) the non-genomic effects of GCs on pathways relevant to the pathogenesis of inflammatory diseases and (ii) the putative role of the membrane GC receptor. Since GC side effects are often considered to be generated through its genomic actions, understanding GC non-genomic effects will help design GCs with a better therapeutic index

The effect of IL-13 and IL-13R130Q, a naturally occurring IL-13 polymorphism, on the gene expression of human airway smooth muscle cells

BACKGROUND: Growing evidence shows that interleukin 13 (IL-13) may play an essential role in the development of airway inflammation and bronchial hyper-responsiveness (BHR), two defining features of asthma. Although the underlying mechanisms remain unknown, a number of reports have shown that IL-13 may exert its deleterious effects in asthma by directly acting on airway resident cells, including epithelial cells and airway smooth muscle cells. In this report, we hypothesize that IL-13 may participate in the pathogenesis of asthma by activating a set of "pro-asthmatic" genes in airway smooth muscle (ASM) cells. METHODS: Microarray technology was used to study the modulation of gene expression of airway smooth muscle by IL-13 and IL-13R130Q. TaqMan™ Real Time PCR and flow cytometry was used to validate the gene array data. RESULTS: IL-13 and the IL-13 polymorphism IL-13R130Q (Arg130Gln), recently associated with allergic asthma, seem to modulate the same set of genes, which encode many potentially interesting proteins including vascular cellular adhesion molecule (VCAM)-1, IL-13Rα2, Tenascin C and Histamine Receptor H1, that may be relevant for the pathogenesis of asthma. CONCLUSIONS: The data supports the hypothesis that gene modulation by IL-13 in ASM may be essential for the events leading to the development of allergic asthma

Mapping physiological G protein-coupled receptor signaling pathways reveals a role for receptor phosphorylation in airway contraction.

G protein-coupled receptors (GPCRs) are known to initiate a plethora of signaling pathways in vitro. However, it is unclear which of these pathways are engaged to mediate physiological responses. Here, we examine the distinct roles of Gq/11-dependent signaling and receptor phosphorylation-dependent signaling in bronchial airway contraction and lung function regulated through the M3-muscarinic acetylcholine receptor (M3-mAChR). By using a genetically engineered mouse expressing a G protein-biased M3-mAChR mutant, we reveal the first evidence, to our knowledge, of a role for M3-mAChR phosphorylation in bronchial smooth muscle contraction in health and in a disease state with relevance to human asthma. Furthermore, this mouse model can be used to distinguish the physiological responses that are regulated by M3-mAChR phosphorylation (which include control of lung function) from those responses that are downstream of G protein signaling. In this way, we present an approach by which to predict the physiological/therapeutic outcome of M3-mAChR-biased ligands with important implications for drug discovery.This study is funded by the Medical Research Council (MRC) through funding of program leaders provided by the MRC Toxicology Unit (to A.B.T.)