Gender and Authorship Categorisation of Arabic Text from Twitter Using Ppm
نویسندگان
چکیده
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.
منابع مشابه
Gender-Preferential Text Mining of E-mail Discourse
This paper describes an investigation of authorship gender attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a Support Vector Machine learning algorithm. Experiments using a corpus of e-mail documents generate...
متن کاملAuthor gender identification from text using Bayesian Random Forest
Nowadays high usage of users from virtual environments and their connection via social networks like Facebook, Instagram, and Twitter shows the necessity of finding out shared subjects in this environment more than before. There are several applications that benefit from reliable methods for inferring age and gender of users in social media. Such applications exist across a wide area of fields,...
متن کاملLanguage and Gender Author Cohort Analysis of E-mail for Computer Forensics
We describe an investigation of authorship gender and language background cohort attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a Support Vector Machine learning algorithm. Experiments using a corpus of e-m...
متن کاملArabic News Articles Classification Using Vectorized-Cosine Based on Seed Documents
Besides for its own merits, text classification (TC) has become a cornerstone in many applications. Work presented here is part of and a pre-requisite for a project we have overtaken to create a corpus for the Arabic text process. It is an attempt to create modules automatically that would help speed up the process of classification for any text categorization task. It also serves as a tool for...
متن کاملHigh capacity steganography tool for Arabic text using 'Kashida'
Steganography is the ability to hide secret information in a cover-media such as sound, pictures and text. A new approach is proposed to hide a secret into Arabic text cover media using "Kashida", an Arabic extension character. The proposed approach is an attempt to maximize the use of "Kashida" to hide more information in Arabic text cover-media. To approach this, some algorithms have been des...
متن کامل