Open AccessIdentification of Urdu Ghazal Poets using SVM
Author(s) -
Nida Tariq,
Iqra Ijaz,
Misbah Malik,
Zubair Malik,
Faisal Bukhari
Publication year - 2020
Publication title -
mehran university research journal of engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2413-7219
pISSN - 0254-7821
DOI - 10.22581/muet1982.1904.07
Subject(s) - urdu , support vector machine , artificial intelligence , identification (biology) , computer science , feature selection , decision tree , style (visual arts) , naive bayes classifier , pattern recognition (psychology) , hindi , poetry , natural language processing , machine learning , literature , art , botany , biology
Urdu literature has a rich tradition of poetry, with many forms, one of which is Ghazal. Urdu poetry structures are mainly of Arabic origin. It has complex and different sentence structure compared to our daily language which makes it hard to classify. Our research is focused on the identification of poets if given with ghazals as input. Previously, no one has done this type of work. Two main factors which help categorize and classify a given text are the contents and writing style. Urdu poets like Mirza Ghalib, Mir Taqi Mir, Iqbal and many others have a different writing style and the topic of interest. Our model caters these two factors, classify ghazals using different classification models such as SVM (Support Vector Machines), Decision Tree, Random forest, Naïve Bayes and KNN (K-Nearest Neighbors). Furthermore, we have also applied feature selection techniques like chi square model and L1 based feature selection. For experimentation, we have prepared a dataset of about 4000 Ghazals. We have also compared the accuracy of different classifiers and concluded the best results for the collected dataset of Ghazals.