Recent developments in information extraction approaches from Arabic tweets on social networking sites

The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more and more in their day-to-day conversations, social media posts and tweets, and it is considered as a way for people to express their sentiment about some certain topics or issues. As a result of the increasing popularity, researchers started to focus their research endeavors on detecting sarcasm from a text in different languages especially the English language. However, the task of sarcasm detection is a challenging task due to the nature of sarcastic texts; which can be relative and significantly differs from one person to another depending on the topic, region, the user's mentality and other factors. In addition to these challenges, sarcasm detection in the Arabic language has its own challenges due to the complexity of the Arabic language, such as being morphologically rich, with many dialects that significantly vary between each other, while also being lowly resourced. In recent years, only few research attempts started tackling the task of sarcasm detection in Arabic, including creating and collecting corpora, organizing workshops and establishing baseline models. This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets, and implementing a new approach for sarcasm detection and quantification in Arabic tweets. The annotation technique followed in this paper is unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; i.e., the model attempts to predict the level of sarcasm instead of binary classification.

Navigating Social Media in #Ophthalmology

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

Since Web 2.0 and the freedom to share information, perspectives, and opinions on global events, services, goods, etc., most of the content on social media platforms comes from users. Social media data includes user sentiment-related subjects. Due to its complexity, ambiguity, dialects, shortage of resources, and morphological diversity, little work has been done on Arabic sentiment analysis. This study introduces a novel word embedding model, AraWord2Vec, based on Wor2Vec trained on Wikipedia and the Sentiment Benchmark dataset. We also examined how Wor2Vec embedding models improved classification tasks. Second, a novel CM_BiLSTM deep learning model called Convolutional Max Pooling and Bidirectional LSTM is presented to enhance Arabic sentiment analysis. Our experimental findings revealed the performance of all models on diverse datasets with an accuracy of 98.47% in binary classification; in multi-classes classification, we obtained a high performance of 98.92%.

Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world's greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered and analyzed the influenza-related tweets written in the Arabic language. Several classifiers were used to measure the quality and the performance of the approach, which are: Naive Bayes, Support Vector Machines, Decision Trees, and K-Nearest Neighbor. The classifiers which achieved the best accuracy results for the three experiments were: Naïve Bayes with 89.06%, and K-Nearest Neighbor with 86.43%, respectively.

The COVID-19 pandemic has spread across the world and has become an international public health emergency. The outbreak of COVID-19 has focused attention on the use of quarantine and social distancing as the primary defense strategies against community infection. Arabic language, as one of the most spoken languages in the world, and the fastest-growing language on the Internet motivates us to provide reliable automated tools that can perform sentiment analysis to reveal users' opinions. This paper was proposed to utilize machine learning (ML) methods for Arabic Sentiment Analysis to understand the positive and negative opinions related to quarantine and social distancing during the outbreak of COVID-19. We provided a model of different essential and ensembles ML classifiers and compared their effectiveness in classifying the collected imbalanced dataset. Moreover, the application of a variety of SMOTe (Synthetic Minority Over-sampling Technique) for our imbalanced dataset was evaluated. The results demonstrated that SMOTe Edited Nearest Neighbor (SMOTEENN) outperformed other SMOTe techniques. Moreover, the results showed that ensemble classifiers are more robust with imbalanced datasets than single classifiers. On the other hand, the overall average of F1 score of single classifiers is more robust than ensemble classifiers when applying SMOTEENN.

Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert the raw tweets into a useful format. Then, the word2vec model is applied to generate the feature vectors. The Bidirectional Gated Recurrent Unit (BiGRU) classifier is utilized to identify and classify the sentiments. Finally, the QPSO algorithm is exploited for the optimal fine-tuning of the hyperparameters involved in the BiGRU model. The proposed QPSODL-SAAT model was experimentally validated using the standard datasets. An extensive comparative analysis was conducted, and the proposed model achieved a maximum accuracy of 98.35%. The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches, such as the Surface Features (SF), Generic Embeddings (GE), Arabic Sentiment Embeddings constructed using the Hybrid (ASEH) model and the Bidirectional Encoder Representations from Transformers (BERT) model.

Sentiment Analysis (SA) is an increasingly important field of study, also known as opinion mining, as it has the ability to identify the emotional tone of a source material as being either positive, negative, or neutral. Simply, SA digests natural languages and extracts insights, making it extremely useful to gain an overview of the public opinion in a certain issue, topic, or even a product. With the vast amount of information over social network platforms, shared views, website reviews, and blogs comes the importance of SA. Arabic is a rich language with extremely complex inflectional and derivational morphology making sentiment analysis in Arabic text more challenging. In this paper, we propose a novel approach to enhance the accuracy of Arabic Sentiment Analysis (ASA). In this aspect, nine supervised machine learning algorithms have been implemented for ASA. Three of these classifiers have never been used before in ASA classification, namely, they are Ridge, Gradient Boosting, and Multi-layer Perceptron. The performance of all nine classifiers is tested using our constructed dataset. The dataset contains 6318 reviews written in different forms of Arabic language and prepared manually by gathering hotel reviews from Booking.com website.

Flood natural disasters that often occur in Jakarta have a bad impact on many sectors. Countermeasures, fast action, and monitoring need to be done to minimize the impact that occurs. Social Media is a technology platform that can provide flood-related data that can be used as primary data or complementary data for monitoring systems. This study focuses on using social media data to be used as flood monitoring data. The analysis used is an analysis with a natural language processing approach. The classification algorithm method used in this study is naive Bayes, random forest, support vector machine, logistic regression, and conditional random field. Location information extraction methods used are Standford NER and Geocoding. This research produces three models. The first model is the classification model used to classify relevant data with an f1- score evaluation value of 82.5%. The second model is the NER model which is used to extract location entities from sentences with an f1-score evaluation value of 73%. The last one is the locator of geocoding with a success rate of 75% for identifying roads. This research also produces a simple dashboard that can be used as a visualization tool.

This study introduces a novel approach to sentiment classification of Arabic tweets regarding educational reforms in Saudi Arabia. The complexity of the Arabic language, with its numerous dialects, poses challenges for natural language processing tasks, particularly when large volumes of data require manual annotation. To overcome the limitations of traditional labeling methods, we developed a weakly supervised learning framework that combines LLMs (GPT-3.5) and pre-trained language models (MarBERT and XLM-RoBERTa) to generate high-quality weakly labeled training data using the Snorkel framework. We fine-tuned the AraBERT model with this weakly labeled data for sentiment classification. Our experimental results demonstrated the effectiveness of the proposed approach, achieving 83% precision, 76% recall, and an 85% F1 score in classifying tweets as positive, negative, or neutral. Comparative analysis showed that GPT-3.5 outperformed Llama 2 in prompting tasks, and our weakly supervised model surpassed baseline machine learning methods. These findings highlight the potential of weakly supervised learning in analyzing public opinion on Arabic social media platforms without relying on large, labeled datasets.

Social media are considered an excellent source of information and can provide opinions, thoughts and insights toward various important topics. Sentiment analysis becomes a hot topic in research due to its importance in making decisions based on opinions derived from analyzing the user’s contents on social media. Although the Arabic language is one of the widely spoken languages used for content sharing across the social media, the sentiment analysis on Arabic contents is limited due to several challenges including the morphological structures of the language, the varieties of dialects and the lack of the appropriate corpora. Hence, the rapid increase in research in Arabic sentiment analysis is grown slowly in contrast to other languages such as English. The contribution of this paper is twofold: First, we introduce a corpus of forty thousand labeled Arabic tweets spanning several topics. Second, we present three deep learning models, namely CNN, LSTM and RCNN, for Arabic sentiment analysis. With the help of word embedding, we validate the performance of the three models on the proposed corpus. The experimental results indicate that LSTM with an average accuracy of 81.31% outperforms CNN and RCNN. Also, applying data augmentation on the corpus increases LSTM accuracy by 8.3%.

Multi-label text classification is a natural extension of text classification in which each document can be assigned with a possible widespread set of labels. Natural Language Processing (NLP) helps to understand and manipulate text in natural language by using the computer. Arabic Text Classification is challenging recently because the Arabic language is under-resourced although it has many users. The aim of this paper is to build a model to classify Arabic news and help users get and display the most relevant news to their interests. In this paper, we demonstrate the efficiency of using deep learning models in solving Arabic multi-label text classification problem. Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) have been used; we build two models via python. All data has been cleaned to improve the quality of experimental data. The result of test data in LSTM was 82.03 whereas in the MLP model was 80.37, and both models were evaluated using F1 score.

Social media provide opportunities for organizations to reach the largest amount of people by measuring the general perception of the consumer and knowing his/her feelings and some of his reactions towards brands and products. On the other hand, these organizations as well as people are keen to preserve the privacy of their data, which will only be achieved by using applications and techniques of artificial intelligence. This scientific paper aims to analyze and discuss the impact of using artificial intelligence applications with their various technologies on social media through the method of critical analysis and evaluation that was applied to previous recent studies that dealt with the impact of artificial intelligence on social media. Then, this paper conducted a critical review of these studies coming up with findings, conclusions, and special recommendations on the subject. This resulted in the fact that studies agree on the great role that artificial intelligence plays in social media platforms in terms of preserving the privacy of the user and organizations and in terms of marketing and increasing the profits of organizations. Moreover, companies that do marketing through social media using artificial intelligence have benefited three times more than other companies that do not. In addition, artificial intelligence contributes to the preservation and security of privacy and data of users and digital owners in social networking sites, and contributes to increasing the profits of companies that use marketing through social media platforms based on artificial intelligence techniques, as their revenues increased by 10%, costs decreased, and productivity and logistic networks improved. This study recommended working to develop the mechanism of artificial intelligence in social media platforms, conducting more studies on the contribution of artificial intelligence in increasing the revenues earned by social media platforms, reducing the costs of social media creation and management, and finally the necessity for the commitment of social media companies to apply artificial intelligence techniques to maintain user privacy.

Morphologically complex languages such as Arabic pose several challenges in Natural Language Processing (NLP) due to their complexity and token sparsity. Most techniques approach the problem by transforming the words of the language from their sparse surface form representation to a less sparse form before processing. The transformation usually takes the form of a morphological analysis or a morphological segmentation. This dissertation addresses two tasks in Arabic NLP: Statistical Machine Translation(SMT) and Sentiment Analysis. To improve English-Arabic SMT, we apply segmentation on Arabic to decrease token sparsity and enhance the correspondence between tokens of the English and Arabic language. However, due to this segmentation, the translation system is limited to extracting features based on morphemes (partial words) and only outputting morphemes during decoding. Such a system lacks knowledge of the original form of the words. We further improve translation from English to Arabic by combining both segmented and desegmented views of the target language. The system can benefit from segmentation's sparsity reduction and verifies its generation of correct words. We present a language-independent technique to desegmentation that approaches the problem as a string transduction task. We propose a new algorithm that desegments the decoder's search space encoded as a lattice, thus allowing the system to use features from the desegmented view of the search space. We extend the phrase-based statistical machine translation system to allow desegmentation during the decoding process on the fly. In addition, we conduct an experimental study to verify what matters most in morphologically segmented SMT models. Our second task is sentiment analysis, where we resort to Arabic lemmatization to improve sentiment analysis of Arabic tweets and blog posts. We explore translation in the opposite direction, from Arabic into English in order to evaluate the loss of sentiment predictability when Arabic social media posts are translated to English, manually or using an SMT system. We use state-of-the-art Arabic and English sentiment Analysis systems and develop automatically generated Arabic lexicons from lemmatized tweets to improve this task.

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.