New techniques for Arabic document classification

Text classification (TC) concerns automatically assigning a class (category) label to a text document, and has increasingly many applications, particularly in the domain of organizing, for browsing in large document collections. It is typically achieved via machine learning, where a model is built on the basis of a typically large collection of document features. Feature selection is critical in this process, since there are typically several thousand potential features (distinct words or terms). In text classification, feature selection aims to improve the computational e ciency and classification accuracy by removing irrelevant and redundant terms (features), while retaining features (words) that contain su cient information that help with the classification task. This thesis proposes binary particle swarm optimization (BPSO) hybridized with either K Nearest Neighbour (KNN) or Support Vector Machines (SVM) for feature selection in Arabic text classi cation tasks. Comparison between feature selection approaches is done on the basis of using the selected features in conjunction with SVM, Decision Trees (C4.5), and Naive Bayes (NB), to classify a hold out test set. Using publically available Arabic datasets, results show that BPSO/KNN and BPSO/SVM techniques are promising in this domain. The sets of selected features (words) are also analyzed to consider the di erences between the types of features that BPSO/KNN and BPSO/SVM tend to choose. This leads to speculation concerning the appropriate feature selection strategy, based on the relationship between the classes in the document categorization task at hand. The thesis also investigates the use of statistically extracted phrases of length two as terms in Arabic text classi cation. In comparison with Bag of Words text representation, results show that using phrases alone as terms in Arabic TC task decreases the classification accuracy of Arabic TC classifiers significantly while combining bag of words and phrase based representations may increase the classification accuracy of the SVM classifier slightly

Hybrid feature selection based on principal component analysis and grey wolf optimizer algorithm for Arabic news article classification

The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a hybrid filter-wrapper method based on Principal Component Analysis (PCA) as a filter approach to select an appropriate and informative subset of features and Grey Wolf Optimizer (GWO) as wrapper approach (PCA-GWO) to select further informative features. Logistic Regression (LR) is used as an elevator to test the classification accuracy of candidate feature subsets produced by GWO. Three Arabic datasets, namely Alkhaleej, Akhbarona, and Arabiya, are used to assess the efficiency of the proposed method. The experimental results confirm that the proposed method based on PCA-GWO outperforms the baseline classifiers with/without feature selection and other feature selection approaches in terms of classification accuracy

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm

Extractive multi document summarization using harmony search algorithm

The exponential growth of information on the internet makes it troublesome for users to get valuable information. Text summarization is the process to overcome such a problem. An adequate summary must have wide coverage, high diversity, and high readability. In this article, a new method for multi-document summarization has been supposed based on a harmony search algorithm that optimizes the coverage, diversity, and readability. Concerning the benchmark dataset Text Analysis Conference (TAC-2011), the ROUGE package used to measure the effectiveness of the proposed model. The calculated results support the effectiveness of the proposed approach

A framework for the Comparative analysis of text summarization techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceWe see that with the boom of information technology and IOT (Internet of things), the size of information which is basically data is increasing at an alarming rate. This information can always be harnessed and if channeled into the right direction, we can always find meaningful information. But the problem is this data is not always numerical and there would be problems where the data would be completely textual, and some meaning has to be derived from it. If one would have to go through these texts manually, it would take hours or even days to get a concise and meaningful information out of the text. This is where a need for an automatic summarizer arises easing manual intervention, reducing time and cost but at the same time retaining the key information held by these texts. In the recent years, new methods and approaches have been developed which would help us to do so. These approaches are implemented in lot of domains, for example, Search engines provide snippets as document previews, while news websites produce shortened descriptions of news subjects, usually as headlines, to make surfing easier. Broadly speaking, there are mainly two ways of text summarization – extractive and abstractive summarization. Extractive summarization is the approach in which important sections of the whole text are filtered out to form the condensed form of the text. While the abstractive summarization is the approach in which the text as a whole is interpreted and examined and after discerning the meaning of the text, sentences are generated by the model itself describing the important points in a concise way

Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization

At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before

Arabic web page clustering: a review

Clustering is the method employed to group Web pages containing related information into clusters, which facilitates the allocation of relevant information. Clustering performance is mostly dependent on the text features' characteristics. The Arabic language has a complex morphology and is highly inflected. Thus, selecting appropriate features affects clustering performance positively. Many studies have addressed the clustering problem in Web pages with Arabic content. There are three main challenges in applying text clustering to Arabic Web page content. The first challenge concerns difficulty with identifying significant term features to represent original content by considering the hidden knowledge. The second challenge is related to reducing data dimensionality without losing essential information. The third challenge regards how to design a suitable model for clustering Arabic text that is capable of improving clustering performance. This paper presents an overview of existing Arabic Web page clustering methods, with the goals of clarifying existing problems and examining feature selection and reduction techniques for solving clustering difficulties. In line with the objectives and scope of this study, the present research is a joint effort to improve feature selection and vectorization frameworks in order to enhance current text analysis techniques that can be applied to Arabic Web pages