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 Practice in the Language Environment l Mumarasah al-Lughah al-‘Arabiyah fi Bi’ah Lughawiyyah

The concept of learning speaking skills was urgently discussed to become a cornerstone in practice. Many students do not understand correctly the concept of speaking Arabic which is the characteristic of someone who is proficient in Arabic. The purpose of this study was to analyze the idea of Mohammed Mohammed Imam Daoud in learning speaking skills in Arabic Language. A qualitative descriptive method was used as the research design. Data analysis techniques included formulating research problems, conducting literature studies, determining units of observation and units of analysis, creating categorization and coding guidelines, coding data, and processing data. The results of the research that the ability to be tired of the meanings of the soul in different approvals and the ability to use vocabulary in the social context. The method of speaking skill was according to Muhammad Daoud's approach to listening skill and speaking skill. Adab of learning the speaking skills are fidelity, seriousness in training, and continuous, so the learners would be skillful. The information would make knowledge but training and practice would create skill cooperation with the teacher and colleagues and humility in their  lives. The key of success in speaking can also be done with the application of Arabic in the language environment. Muhammad Imam Dawood's idea of teaching speaking skills was important for developing the quality of learning speaking skills at the primary, intermediate and advanced levels.  The application of Arabic in practice would increase the fluently in speaking performance

Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques

Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and (b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be done in the future

Sentiment Analysis of Multilingual Dataset of Bahraini Dialects, Arabic, and English

Data Availability Statement: The dataset is openly available at: https://data.mendeley.com/datasets/5rhw2srzjj (accessed on 15 February 2023). Dataset: https://doi.org/10.17632/5rhw2srzjj.1 Dataset License: CC-BY-NC.Copyright © 2023 by the authors. Sentiment analysis is an application of natural language processing (NLP) that requires a machine learning algorithm and a dataset. In some cases, the dataset availability is scarce, particularly with Arabic dialects, precisely the Bahraini ones, which necessitates using an approach such as translation, where a rich source language is exploited to create the target language dataset. In this study, a dataset of Amazon product reviews in Bahraini dialects is presented. This dataset was generated using two cascading stages of translation—a machine translation followed by a manual one. Machine translation was applied using Google Translate to translate English Amazon product reviews into Standard Arabic. In contrast, the manual approach was applied to translate the resulting Arabic reviews into Bahraini ones by qualified native speakers utilizing constructed customized forms. The resulting parallel dataset of English, Standard Arabic, and Bahraini dialects is called English_Modern Standard Arabic_Bahraini Dialects product reviews for sentiment analysis “E_MSA_BDs-PR-SA”. The dataset is balanced, composed of 2500 positive and 2500 negative reviews. The sentiment analysis process was implemented using a stacked LSTM deep learning model. The Bahraini dialect product dataset can be utilized in the transfer learning process for sentimentally analyzing another dataset in Bahraini dialects.This research received no external funding