Classification of Al-Hadith Al-Shareef using data mining algorithm

In this paper we compared the effectiveness of four different automatic learning algorithms for classifying Al-Hadith Al-Shareef into 8 selective books depending on Sahih BuKhari.The automatic learning algorithms are Rocchio algorithm, K-NN algorithm (K- Nearest Neighbor), Naïve Bayes algorithm and SVM algorithm (Support Vector Machines). We used TF-IDF technique to compute the relative frequency for each word in a particular document. We split the documents of AL-Hadith in such 75% of AL-Hadiths (1350 Hadiths) are used as training data (build the classifier) and the remaining 25% of AL-Hadith (150 Hadiths) are used for testing the accuracy of the resulting models in reproducing the manual category assignments.The average of words in each document is about 5to10 words

Arabic Educational Neural Network Chatbot

Chatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers all questions about educational regulations in the United Arab Emirates

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

A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change

Smart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public perceive current affairs. The ongoing research in this domain has leveraged multiple types of sentiment analysis. However, although the existing approaches enable researchers to acquire retrospective insights into public opinion, they do not enable a real-time evaluation. In addition, they are not readily scalable and necessitate the analysis of a significant amount of posts (in the millions) to facilitate a more in-depth evaluation. The study outlined in this paper was designed to address these shortcomings by presenting a framework that facilitates a real-time evaluation of the evolution of opinions shared by prominent public features and their respective followers; that is, high-impact posts. The developed solution encompasses a sophisticated Bi-directional LSTM classifier that was implemented and tested using a dataset consisting of 278,000 tweets related to the topic of climate change. The outcomes reveal that the proposed classifier achieved the following accuracies: 88.41% for discrimination; 89.66% for anger; 87.01% for inspiration; and 87.52% for joy - with negative emotions being more accurately classified than positive emotions. Similarly, the sentiment classification performance yielded accuracies of 89.32% for support and 89.80% for strong support, as well as 88.14% for opposition and 87.52% for strong opposition. In addition, the findings of the study indicated that geographic location, chosen topic, cultural sensitivities, and posting frequency all play a critical role in public reactions to posts and the ensuing perspectives they adopt. The solution stands out from existing retrospective analysis methods because it does not rely on the analysis of vast quantities of data records; rather, it can perform real-time, high-impact content analysis in a resource-efficient and sustainable manner. This framework can be used to generate insights into how public opinion is developing on a real-time basis. As such, it can have meaningful application within social media analysis efforts

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence

An Arabic social media based framework for incidents and events monitoring in smart cities

Smart city initiatives aim at leveraging human, collective, and technological capital to ensure sustainable development and quality of life for their citizens. Offering efficient and sustainable emergency rescue services in smart cities requires coordinated efforts and shared information between the public, the decision makers, and rescue teams. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose a novel framework for events and incidents\u27 management in smart cities. Our framework uses text mining, text classification, named entity recognition, and stemming techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the data is automatically collected from social media feeds then processed to generate incident intelligence reports that can provide emergency situational awareness and early warning signs to rescue teams. The proposed framework was implemented and tested using datasets collected from Arabic Twitter feeds over a two-years span, and the obtained results show that Polynomial Networks and Support Vector Machines are the top performers in terms of Arabic text classification, achieving classification accuracy of 96.49% and 94.58% respectively, when used with stemming. The results also showed that the use of stemming led to a penalty in terms of response time, and that the richer the dataset/corpus used in terms of size and composition, the higher the classification accuracy will be

Combining Named Entity Recognition and Emotion Analysis of Tweets for Early Warning of Violent Actions

Natural Language Processing techniques have gained popularity for the analysis of social media content. A number of techniques have been proposed to analyze various aspects such as public opinion, sentiments, and emotions expressed, opinion leaders, and extreme views. However, existing approaches take a retrospective approach that studies opinions after the occurrence of events. With the buildup of negative sentiments and extreme public opinion potentially leading to violent actions and civil disobedience, there is a need for a proactive and predictive approach that can offer early warning signs to government officials to intervene. In this work, we propose such an approach by combining two natural language processing techniques: Named entity recognition (NER) and emotions analysis. By tagging important entities within posts, such as prominent figures and important locations, and analyzing whether the tweets mentioning important entities carry negative emotions such as anger or violence, we are able to give insights about potential violent actions. Our framework was built and tested using 1290 tweets related to the 2020 US presidential election and the related US Capitol attack. The results obtained are promising and open the door for early intervention and appropriate preparedness for violent actions that may ensue from the buildup of negative sentiments and public views

A Hybrid Machine Learning Approach for Sentiment Analysis of Partially Occluded Faces

With millions of images and videos uploaded on social media every day, facial sentiment analysis has gained significant attention as means of gaining large scale insights into people\u27s emotions and sentiments. While several models have been proposed for sentiment and emotion analysis of complete, camera-facing pictures, the analysis of images appearing in natural settings and crowded scenes poses more challenges. In such settings, images typically contain a mix of complete and partially occluded faces (i.e. obstructed faces) presented with different angles, resolutions and distances from the camera. In this paper, we propose a hybrid machine learning model combining convolutional neural networks (CNNs) and support vector machines (SVMs) to achieve accurate facial sentiment and emotion analysis of incomplete and partially occluded facial images. The proposed model was successfully tested using 4, 690 images containing 25, 400 faces, collected from a large-scale public event. The model was able to correctly classify the test dataset containing faces with different angles, camera distances, occlusion areas, and image resolutions. The results show a classification accuracy of 89.9% for facial sentiment analysis, and an accuracy of 87.4% when distinguishing between seven emotions in partially occluded faces. This makes our model suitable for real-life practical applications

A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events

Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve accurate sentiment and emotion analysis of facial images. The proposed model was successfully tested using 3,750 images containing 22,563 faces, collected from a large consumer electronics trade show. The model was able to correctly classify the test images which contained faces with different angles, distances, occlusion areas, facial orientation and resolutions. It achieved an average accuracy of 90.6% when distinguishing between seven emotions (Happiness, smiling, laughter, neutral, sadness, anger, and surprise) in complete faces, and 86.16% accuracy in partially occluded faces. Such model can be leveraged for the automatic analysis of attendees’ engagement level in events. Furthermore, it can open the door for many useful applications in smart cities, such as measuring employees’ satisfaction and citizens’ happiness

A sentiment reporting framework for major city events: Case study on the China-United States trade war

2020 Elsevier Ltd Smart cities are conceptualized as a vehicle for sustainable urban development and a means to deliver high quality of life for residents. One of the core functions of a smart city consists in the continuous monitoring of events, assets and people and the use of this information and intelligence for the streamlining of the city\u27s operations. Public opinion represents one type of intelligence of particular importance and value. By monitoring public opinion, governments seek to understand prevalent views about the current events and policies, as well as identify extreme views and trends that may represent problematic situations or precursors to violent actions. Ultimately, maintaining a constant awareness of public opinion means that authorities can better assess and predict public reactions in relation to ongoing events, and thus take appropriate actions to maintain public safety. Due to the popular use of social media to express sentiments and emotions about current events, social media content analysis has been contemplated as a promising solution to capture public opinion. However, existing approaches take a coarse-grained retrospective approach to social media content analysis. Furthermore, those approaches suffer from the lack of scalability and efficiency, as they necessitate the collection and analysis of large volumes of social media content (often millions of posts), to come up with relevant conclusions. In this work, we address those limitations by proposing a novel framework for the real-time monitoring of public opinion. To ensure efficiency and scalability, our framework focuses on the analysis of high impact social media content generated by opinion leaders and their followers as means to offer in-depth insights and sentiment intelligence reports about events, as they are occurring in real time. The proposed framework was implemented and tested on data harvested from 52 economic opinion leaders, with a focus on the China-US trade war as case study. The results show that the convolutional neural network (CNN) classifier used for sentiment analysis yielded a classification accuracy of 86% when differentiating between four sentiment categories: Support, strong support, dissent, and strong dissent. The Support Vector Machine (SVM) classifier employed to perform in-depth emotional analysis attained an accuracy of 82% when differentiating between five emotions: Angry, depressed, excited, happy, and worried. Unlike existing retrospective social media analysis approaches that require the analysis of millions of posts, our approach focuses on the analysis of high-impact social media content in real-time, thus constituting an efficient, sustainable, and timely solution to public opinion monitoring