Effects of term weighting approach with and without stop words removing on Arabic text classification

Classifying text is a method for categorizing documents into pre-established groups. Text documents must be prepared and represented in a way that is appropriate for the algorithms used for data mining prior to classification. As a result, a number of term weighting strategies have been created in the literature to enhance text categorization algorithms' functionality. This study compares the effects of Binary and Term frequency weighting feature methodologies on the text's classification method when stop words are eliminated once and when they are not. In recognition of assessing the effects of prior weighting of features approaches on classification results in terms of accuracy, recall, precision, and F-measure values, we used an Arabic data set made up of 322 documents divided into six main topics (agriculture, economy, health, politics, science, and sport), each of which contains 50 documents, with the exception of the health category, which contains 61 documents. The results demonstrate that for all metrics, the term frequency feature weighting approach with stop word removal outperforms the binary approach, while for accuracy, recall, and F-Measure, the binary approach outperforms the TF approach without stop word removal. However, for precision, the two approaches produce results that are very similar. Additionally, it is clear from the data that, using the same phrase weighting approach, stop word removing increases classification accuracy

Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language

Text classification (TC) is a crucial subject. The number of digital files available on the internet is enormous. The goal of TC is to categorize texts into a series of predetermined groups. The number of studies conducted on the English database is significantly higher than the number of studies conducted on the Arabic database. Therefore, this research analyzes the performance of automatic TC of the Arabic language using Machine Learning (ML) approaches. Further, Single-label Arabic News Articles Datasets (SANAD) are introduced, which contain three different datasets, namely Akhbarona, Khaleej, and Arabiya. Initially, the collected texts are pre-processed in which tokenization and stemming occur. In this research, three kinds of stemming are employed, namely light stemming, Khoja stemming, and no- stemming, to evaluate the effect of the pre-processing technique on Arabic TC performance. Moreover, feature extraction and feature weighting are performed; in feature weighting, the term weighting process is completed by the term frequency- inverse document frequency (tf-idf) method. In addition, this research selects C4.5, Support Vector Machine (SVM), and Naïve Bayes (NB) as a classification algorithm. The results indicated that the SVM and NB methods had attained higher accuracy than the C4.5 method. NB achieved the maximum accuracy with a performance of 99.9%

Arabic text classification methods: Systematic literature review of primary studies

Recent research on Big Data proposed and evaluated a number of advanced techniques to gain meaningful information from the complex and large volume of data available on the World Wide Web. To achieve accurate text analysis, a process is usually initiated with a Text Classification (TC) method. Reviewing the very recent literature in this area shows that most studies are focused on English (and other scripts) while attempts on classifying Arabic texts remain relatively very limited. Hence, we intend to contribute the first Systematic Literature Review (SLR) utilizing a search protocol strictly to summarize key characteristics of the different TC techniques and methods used to classify Arabic text, this work also aims to identify and share a scientific evidence of the gap in current literature to help suggesting areas for further research. Our SLR explicitly investigates empirical evidence as a decision factor to include studies, then conclude which classifier produced more accurate results. Further, our findings identify the lack of standardized corpuses for Arabic text; authors compile their own, and most of the work is focused on Modern Arabic with very little done on Colloquial Arabic despite its wide use in Social Media Networks such as Twitter. In total, 1464 papers were surveyed from which 48 primary studies were included and analyzed

Applications of Mining Arabic Text: A Review

Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents

Mining Twitter for crisis management: realtime floods detection in the Arabian Peninsula

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of doctor of Philosophy.In recent years, large amounts of data have been made available on microblog platforms such as Twitter, however, it is difficult to filter and extract information and knowledge from such data because of the high volume, including noisy data. On Twitter, the general public are able to report real-world events such as floods in real time, and act as social sensors. Consequently, it is beneficial to have a method that can detect flood events automatically in real time to help governmental authorities, such as crisis management authorities, to detect the event and make decisions during the early stages of the event. This thesis proposes a real time flood detection system by mining Arabic Tweets using machine learning and data mining techniques. The proposed system comprises five main components: data collection, pre-processing, flooding event extract, location inferring, location named entity link, and flooding event visualisation. An effective method of flood detection from Arabic tweets is presented and evaluated by using supervised learning techniques. Furthermore, this work presents a location named entity inferring method based on the Learning to Search method, the results show that the proposed method outperformed the existing systems with significantly higher accuracy in tasks of inferring flood locations from tweets which are written in colloquial Arabic. For the location named entity link, a method has been designed by utilising Google API services as a knowledge base to extract accurate geocode coordinates that are associated with location named entities mentioned in tweets. The results show that the proposed location link method locate 56.8% of tweets with a distance range of 0 – 10 km from the actual location. Further analysis has shown that the accuracy in locating tweets in an actual city and region are 78.9% and 84.2% respectively