Arabic Tweets Categorization Based on Rough Set Theory

Bekkali, Mohammed; Lachkar, Abdelmonaime; Abdellah, Ben

doi:10.5121/csit.2014.41109

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Here are various ways to gather the user's data and do the classifying textual data. A number of recent papers have addressed the classification of tweets most of them were tested against English and Arabic text (Bekkali & Lachkar, 2014). According to (Ur-Rahman & Harding, 2012), text classification is an important approach to handling textual data or information in the overall process of knowledge discovery from textual databases.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Menaouer

Zahra

Mohammed

2022

International Journal of Service Science, Management, Engineering, and Technology

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Social media has revolutionized the way people disclose their personal health concerns and express opinions on public health issues. In this paper a new approach for multi-class sentiment classification using supervised learning techniques. The aim of this multi-class sentiment classification is to assign the healthcare Tweets automatically into predetermined categories on the basis of their linguistic characteristics, their contents, and some of the words that characterize each category from the others. Briefly, relevant health datasets are collected from Twitter using Twitter API; then, use of the methodology is illustrated and evaluated against one with only three different algorithms was used, to improve the accuracy of Decision Trees, SMO, and K-NN classifiers. Many experiments accomplished to prove the validity and efficiency of the approach using datasets tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Menaouer

Zahra

Mohammed

2022

International Journal of Service Science, Management, Engineering, and Technology

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“…Research into processing Arabic twitter text was aided when Twitter started to support Arabic hashtags. The study by Bekkali and Lachkar [24] applied twitter text categorisation based on applying rough set theory using the NB and SVM classifiers. The study shows that applying the upper approximation of rough set theory increases the F1-measure.Hussienet al [25]applied sentiment classification to twitter text.…”

Section: Background and Related Workmentioning

confidence: 99%

Gender and Authorship Categorisation of Arabic Text from Twitter Using PPM

Al-Tamimi¹,

Teahan²

2017

IJCSIT

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In this paper we present gender and authorship categorisationusing the Prediction by Partial Matching (PPM) compression scheme for text from Twitter written in Arabic. The PPMD variant of the compression scheme with different orders was used to perform the categorisation. We also applied different machine learning algorithms such as Multinational Naïve Bayes (MNB), K-Nearest Neighbours (KNN), and an implementation of Support Vector Machine (LIBSVM), applying the same processing steps for all the algorithms. PPMD shows significantly better accuracy in comparison to all the other machine learning algorithms, with order 11 PPMD working best, achieving 90 % and 96% accuracy for gender and authorship respectively.

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“…Two major approaches have been proposed to deal with the sparseness of short text. One aim to bring additional semantics from the dataset itself, this approach require Natural Language Processing techniques [14] [23]. The second try to enrich the representation of short text with additional information derived from existing large corpus or ontologies [16] [17] [20] [24] [25].…”

Section: Introductionmentioning

confidence: 99%

Formal Concept Analysis and Rough Set Theory for an Effective Representation of Arabic Short Text

Bekkali¹,

Ensa²

2019

IJATCSE

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With the increase use of mobile devices, such as smartphones and tablets, short text messages, tweets, comments and so on, have become a large portion of the online text data. Today, about one billion users interact daily on online social networks, where they share information and discuss a wide variety of topics. As in the rest of the world, users in Arab countries engage in social media applications. The representation of these short texts plays a vital role in language understanding. As a result, it may affect positively or negatively the performance of any Arabic text mining task. In this paper, we propose an efficient Arabic short text based on Formal Concept Analysis (FCA), which is a mathematical tool that offers conceptual data for knowledge representation, extraction and analysis with applications in different areas; combined with Rough Set Theory (RST) as a mathematical tool to deal with imprecise and vague data. The obtained results illustrate the interest of our contribution.

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Arabic Tweets Categorization Based on Rough Set Theory

Abstract: Twitter is a popular microblogging service where users create status messages (called "tweets")

Cited by 5 publications

References 22 publications

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Gender and Authorship Categorisation of Arabic Text from Twitter Using PPM

Formal Concept Analysis and Rough Set Theory for an Effective Representation of Arabic Short Text

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