Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).

Using AraGPT and ensemble deep learning model for sentiment analysis on Arabic imbalanced dataset

With the fast growth of mobile technology, social media has become important for people to share their thoughts and feelings. Businesses and governments can make better strategic decisions when they know what the public thinks. Because of this, sentiment analysis is an important tool for figuring out how different people’s opinions are. This article presents a deeplearning ensemble model for sentiment analysis. The ensemble model proposed consists of three deep-learning models, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), as base classifiers. AraBERT is responsible for presenting the textual input data into representative embeddings. The stacking ensemble model then captures the long-range dependencies in the embedding for a given class. As a meta-classifier, Support Vector Machine (SVM) then combines the predictions made by the stacking deep learning model. In addition, data augmentation with AraGPT was implemented to address the imbalanced dataset issues. The experimental results demonstrate that the proposed model outperforms the state-of-the-art models with an accuracy of 88.89%, 90.88%, and 88.23% on the HARD, BRAD, and Twitter datasets, respectively

Using AraGPT and ensemble deep learning model for sentiment analysis on Arabic imbalanced dataset

With the fast growth of mobile technology, social media has become important for people to share their thoughts and feelings. Businesses and governments can make better strategic decisions when they know what the public thinks. Because of this, sentiment analysis is an important tool for figuring out how different people’s opinions are. This article presents a deeplearning ensemble model for sentiment analysis. The ensemble model proposed consists of three deep-learning models, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), as base classifiers. AraBERT is responsible for presenting the textual input data into representative embeddings. The stacking ensemble model then captures the long-range dependencies in the embedding for a given class. As a meta-classifier, Support Vector Machine (SVM) then combines the predictions made by the stacking deep learning model. In addition, data augmentation with AraGPT was implemented to address the imbalanced dataset issues. The experimental results demonstrate that the proposed model outperforms the state-of-the-art models with an accuracy of 88.89%, 90.88%, and 88.23% on the HARD, BRAD, and Twitter datasets, respectively

Dual antiplatelet therapy duration after coronary stenting in clinical practice: results of an EAPCI survey

Aims: Our aim was to report on a survey initiated by the EuropeanAssociation of Percutaneous Cardiovascular Interventions (EAPCI) concerning opinion on the evidence relating to dual antiplatelet therapy (DAPT) duration after coronary stenting.Methods and results: Results from three randomised clinical trials were scheduled to be presented at the American Heart Association Scientific Sessions 2014 (ARIA 2014). A web-based survey was distributed to all individuals registered in the EuroIntervention mailing list (n=15,200) both before and after ARIA 2014. A total of 1,134 physicians responded to the first (i.e., before AHA 2014) and 542 to the second (i.e., after ARIA 2014) survey. The majority of respondents interpreted trial results consistent with a substantial equipoise regarding the benefits and risks of an extended versus a standard DAPT strategy. Two respondents out of ten believed extended DAFT should be implemented in selected patients. After ARIA 2014, 46.1% of participants expressed uncertainty about the available evidence on DAFT duration, and 40.0% the need for clinical guidance.Conclusions: This EAPCI survey highlights considerable uncertainty within the medical community with regard to the optimal duration of DAFT after coronary stenting in the light of recent reported trial results. Updated recommendations for practising physicians to guide treatment decisions in routine clinical practice should be provided by international societies