حوراء علي طاهر الشمرتي

Abstract- In computer science, retrieving Arabic information has become a major research topic. Searching and retrieving knowledge-based information from databases is known as information retrieval (IR). However, the influence of the stop word removal part has a high impact on the retrieved Arabic text. In order to demonstrate the effect of stop words in Arabic text, we used one of the most commonly used techniques, the Jaccard technique, in this paper. We used the Jaccard technique in two ways, the first with Arabic stop words and the second way without stop words. In the experiments, we collected property Arabic datasets manually. The Arabic information retrieval system showed promising results for Arabic texts in similarity between documents and texts. It gave higher accuracy while removing stop words compared to texts containing stop words.

Topic modeling from texts is one of the important topics in natural language processing (NLP), as it plays a fundamental role in summarizing texts, understanding their content, and facilitating access to the main ideas, especially in light of the vast quantity of unstructured texts available today. Extracting titles is used in a variety of fields, such as news archiving, document classification, and content analysis in social media, making it an essential tool for improving information management and effective presentation. In this research, we focused on improving the methodology for extracting titles from texts by integrating two leading techniques: the topic assignment model using Latent Dirichlet Allocation (LDA) and the named entity recognition technique (NER). This combination aims to achieve a balance between identifying general topics of texts via LDA and extracting important information and key entities using NER, ensuring the generation of accurate and understandable titles that better reflect the actual content of the texts. The results of the study showed that the combined methodology achieved an accuracy of 71.97%, outperforming the performance of each technique separately, where the accuracy of NER alone was 29.78% and the accuracy of LDA alone was 67.80%. These results underscore the importance of integrating different techniques into NLP to improve headline extraction performance. This approach contributes to the development of more efficient text analysis methods, which enhances NLP applications in areas such as news analysis, content management, and document summarization, highlighting the importance of the topic in improving the handling of large texts and presenting them in a clearer and more appropriate way.

Sentiment analysis (SA) is a growing area of study that straddles a number of disciplines, including machine learning, data mining, and natural language processing. It is focused on the automatic extraction of viewpoints presented in a certain text. Many studies have been conducted in the area of sentiment analysis because to its broad uses, particularly on texts in English, whereas other languages like Arabic have gotten less attention. The Arabic language presents several difficulties, such as its rich morphology and the difficulty of tracing words back to their original roots. Arabic comments have been analyzed and categorized into good and negative attitudes using a framework. With the aim of evaluating any tweet, opinion, purpose, or reputation, such as a university, company, mobile, and others, the research analyzes the comments made by users of the social networking site Twitter. It does this by using classification technology and machine learning, which are among the fundamental tasks of the data mining process used in the larger process, which is to explore knowledge. This search helps the user to access the evaluation of other users through their tweets and comments on the social networking site for an opinion immediately and automatically, and then the process of uploading and evaluating the opinions using appropriate algorithms for this purpose as (Decision Tree classifier DTC, XGboost, Logistic Regression LR, Support Vector Machine SVM) with Term Frequency-Inverse Document Frequency TF_IDF

One application of Natural Language Processing (NLP) is Next Word Prediction, also known as Language Modeling. This process involves predicting the most likely word to follow in a given sentence based on the preceding context. It has numerous widely used applications, like auto correct, which is mostly used in emails and messages. It can also be used in Microsoft Word or Google searches to predict the next word based on past searches or global queries. The goal of Natural Language Generation (NLG) is to create language that is human-interpretable and natural. Users find text generation, and next-word prediction in particular, convenient as it makes typing faster and error-free. Consequently, an essential analysis topic for all languages is a personalized text prediction system. This paper suggests a novel approach for predicting the following word in a Arabic sentence. It is possible to minimize the total number of keystrokes a user makes by anticipating the next word in a sequence. In this work, Bert algorithm and Continuous Bag of Words(CBOW) are proposed to predict the next word in Arabic language, and predict the previous word. The Bert Algorithm is achieved the best accuracy , 90% for next word prediction, and 80% for previous word prediction . And, Continuous Bag of Words(CBOW) is achieved the best accuracy , 100% for next word prediction, and 100% for previous word prediction

Abstract The rapid spread of the COVID-19 pandemic across the globe has resulted in the widespread dissemination of misinformation and fake news related to the virus. This misinformation has caused significant confusion among citizens, as well as heightened fear and anxiety. Consequently, it is crucial to develop automatic methods capable of effectively detecting such misinformation. Various machine learning (ML) and deep learning (DL) approaches have been implemented, each with its strengths and weaknesses. Combining multiple approaches has shown promising results. In this study, a hybrid model combining a multi-layer perceptron (MLP) and a support vector machine (SVM) was proposed to detect Arabic fake news using the ArCOVID19-Rumors dataset. The MLP was used to extract relevant features, while the SVM was employed to make the final classification decision. The proposed method also leverages Doc2Vec technology, a widely used document embedding technique, to extract features and convert them into numerical vectors while preserving the semantic and syntactic information of the documents. The experiment demonstrated that the hybrid MLP-SVM model outperformed related models when implemented independently. The results showed that the hybrid model achieved an accuracy of 87%, surpassing the performance of standalone MLP (84%) and SVM (83%) models. These results were validated using multiple metrics, including precision, recall, F1-score, and accuracy. The study indicates the importance of combining multiple algorithms for detecting fake news, as integrating the strengths of different techniques can lead to significantly improved classification performance.

Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Ar-abic countries as 21 classes. Three types of classifiers (Naïve Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second method-ology with clustering, the used data set is NADI shared task data set.

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