The International Arab Journal of Information Technology (IAJIT)


Designing Punjabi Poetry Classifiers Using Machine Learning and Different Textual Features

Analysis of poetic text is very challenging from computational linguistic perspective. Computational analysis of literary arts, especially poetry, is very difficult task for classification. For library recommendation system, poetries can be classified on various metrics such as poet, time period, sentiments and subject matter. In this work, content-based Punjabi poetry classifier was developed using Weka toolset. Four different categories were manually populated with 2034 poems Nature and Festival (NAFE), Linguistic and Patriotic (LIPA), Relation and Romantic (RORE), Philosophy and Spiritual (PHSP) categories consists of 505, 399, 529 and 601 numbers of poetries, respectively. These poetries were passed to various pre-processing sub phases such as tokenization, noise removal, stop word removal, and special symbol removal. 31938 extracted tokens were weighted using Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme. Based upon poetry elements, three different textual features (lexical, syntactic and semantic) were experimented to develop classifier using different machine learning algorithms. Naive Bayes (NB), Support Vector Machine, Hyper pipes and K-nearest neighbour algorithms were experimented with textual features. The results revealed that semantic feature performed better as compared to lexical and syntactic. The best performing algorithm is SVM and highest accuracy (76.02%) is achieved by incorporating semantic information associated with words.

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[28] Unicode Table., from info/unicode-decimal.html, Last Visited, 2015. Jasleen Kaur had done Bachelor of Technology, Computer Science and Engineering from Guru Teg Bahadur Khalsa Institute of Engineering Technology, Malout, Punjab and Master of Technology (Computer Engineering) from Punjabi University, Patiala, Punjab. She had completed her PhD. from Uka Tarsadia University, Bardoli, Gujarat. She has published 15 papers in various International Journals and had more than 60 citations. She had publications with Indersicence Publishers, Springer and ACM digital Library. Jatinderkumar Saini is Ph.D. from VNSGU, Surat. He secured First Rank in all three years of MCA and has been awarded Gold Medals forthis. Besides being University Topper, he is IBM Certified Database Associate (DB2) as well as IBM Certified Associate Developer (RAD). Associated with more than 50countries, he has been the Member of Program Committee for more than 50 International Conferences (including those by IEEE) and Editorial Board Member or Reviewer for more than 30 International Journals (including many those with Thomson Reuters Impact Factor). He has more than 55 research paper publications and nearly 20 presentations in reputed International and National Conferences and Journals. He is member of ISTE, IETE, ISG and CSI.