Search Results (12)

People tend to share their opinions on social media daily. This text needs to be accurately mined for different purposes like enhancements in services and/or products. Mining and analyzing Arabic text have been a big challenge due to many complications inherited in Arabic language. Although, many research studies have already investigated the Arabic text sentiment analysis problem, this paper investigates the specific research topic that addresses Arabic comparative opinion mining. This research topic is not widely investigated in many research studies. This paper proposes a lexicon-based framework which includes a set of proposed algorithms for the mining and analysis of Arabic comparative sentences. The proposed framework comprises a set of contributions including an Arabic comparative sentence keywords lexicon and a proposed algorithm for the identification of Arabic comparative sentences, followed by a second proposed algorithm for the classification of identified comparative sentences into different types. The framework also comprises a third proposed algorithm that was developed to extract relations between entities in each of the identified comparative sentence types. Finally, two proposed algorithms were developed for the extraction of the preferred entity in each sentence type. The framework was evaluated using three different Arabic language datasets. The evaluation metrics used to obtain the evaluation results include precision, recall, F-score, and accuracy. The average values of the evaluation metrics for the proposed sentences identification algorithm reached 97%. The average evaluation values of the evaluation metrics for the proposed sentence type identification algorithm reached 96%. Finally, the average results showed 97% relation word extraction precision for the proposed relation extraction algorithm. Full article

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure. Full article

Recently, business sectors have focused on offering a wide variety of services through utilizing different modern technologies such as super apps in order to fulfill customers’ needs and create a satisfactory user experience. Accordingly, studying the user experience has become one of the most popular trends in the research field due to its essential role in business prosperity and continuity. Thus, many researchers have dedicated their efforts to exploring and analyzing the user experience across social media, blogs, and websites, employing a variety of research methods such as machine learning to mine users’ reviews. However, there are limited studies concentrated on analyzing super app users’ experiences and specifically mining Arabic users’ reviews. Therefore, this paper aims to analyze and discover the most important topics that affect the user experience in the super app environment by mining Arabic business sector users’ reviews in Saudi Arabia using biterm topic modeling, CAMeL sentiment analyzer, and doc2vec with k-means clustering. We explore users’ feelings regarding the extracted topics in order to identify the weak aspects to improve and the strong aspects to enhance, which will promote a satisfactory user experience. Hence, this paper proposes an Arabic text annotation framework to help the business sector in Saudi Arabia to determine the important topics with negative and positive impacts on users’ experience. The proposed framework uses two approaches: topic modeling with sentiment analysis and topic modeling with clustering. As a result, the proposed framework reveals four important topics: delivery and payment, customer service and updates, prices, and application. The retrieved topics are thoroughly studied, and the findings show that, in most topics, negative comments outweigh positive comments. These results are provided with general analysis and recommendations to help the business sector to improve its level of services. Full article

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset. Full article

Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information. Full article

With the increasing use of machine learning across various fields to address several aims and goals, the complexity of the ML and Deep Learning (DL) approaches used to provide solutions has also increased. In the last few years, Explainable AI (XAI) methods to further justify and interpret deep learning models have been introduced across several domains and fields. While most papers have applied XAI to English and other Latin-based languages, this paper aims to explain attention-based long short-term memory (LSTM) results across Arabic Sentiment Analysis (ASA), which is considered an uncharted area in previous research. With the use of Local Interpretable Model-agnostic Explanation (LIME), we intend to further justify and demonstrate how the LSTM leads to the prediction of sentiment polarity within ASA in domain-specific Arabic texts regarding medical insights on LASIK surgery across Twitter users. In our research, the LSTM reached an accuracy of 79.1% on the proposed data set. Throughout the representation of sentiments using LIME, it demonstrated accurate results regarding how specific words contributed to the overall sentiment polarity classification. Furthermore, we compared the word count with the probability weights given across the examples, in order to further validate the LIME results in the context of ASA. Full article

Questions are crucial expressions in any language. Many Natural Language Processing (NLP) or Natural Language Understanding (NLU) applications, such as question-answering computer systems, automatic chatting apps (chatbots), digital virtual assistants, and opinion mining, can benefit from accurately identifying similar questions in an effective manner. We detail methods for identifying similarities between Arabic questions that have been posted online by Internet users and organizations. Our novel approach uses a non-topical rule-based methodology and topical information (textual similarity, lexical similarity, and semantic similarity) to determine if a pair of Arabic questions are similarly paraphrased. Our method counts the lexical and linguistic distances between each question. Additionally, it identifies questions in accordance with their format and scope using expert hypotheses (rules) that have been experimentally shown to be useful and practical. Even if there is a high degree of lexical similarity between a When question (Timex Factoid—inquiring about time) and a Who inquiry (Enamex Factoid—asking about a named entity), they will not be similar. In an experiment using 2200 question pairs, our method attained an accuracy of 0.85, which is remarkable given the simplicity of the solution and the fact that we did not employ any language models or word embedding. In order to cover common Arabic queries presented by Arabic Internet users, we gathered the questions from various online forums and resources. In this study, we describe a unique method for detecting question similarity that does not require intensive processing, a sizable linguistic corpus, or a costly semantic repository. Because there are not many rich Arabic textual resources, this is especially important for informal Arabic text processing on the Internet. Full article

Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios. Full article

Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion that was dominant or most expressed in both places. However, a larger proportion of users living in Mecca fell under this category. The proposed analysis was an initial attempt toward studying the emotional and behavioral differences between users living in different cities of Saudi Arabia. This study has several other important applications. First, the emotion-based study could contribute to the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a particular user. Full article

Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The modern writing style of Arabic excludes the diacritical marking, without which Arabic words become ambiguous. For a search query, the user has to skim over the document to infer if the word has the same meaning they are after, which is a time-consuming task. It is hoped that clustering the retrieved documents will collate documents into clear and meaningful groups. In this paper, we use an enhanced k-means clustering algorithm, which yields a faster clustering time than the regular k-means. The algorithm uses the distance calculated from previous iterations to minimize the number of distance calculations. We propose a system to cluster Arabic search results using the enhanced k-means algorithm, labeling each cluster with the most frequent word in the cluster. This system will help Arabic web users identify each cluster’s topic and go directly to the required cluster. Experimentally, the enhanced k-means algorithm reduced the execution time by 60% for the stemmed dataset and 47% for the non-stemmed dataset when compared to the regular k-means, while slightly improving the purity. Full article

Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods. Full article

Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures. Full article