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Twitter has become the foremost standard of social media in today’s world. Over 335 million users are online monthly, and near about 80% are accessing it through their mobiles. Further, Twitter is now supporting 35+ which enhance its usage too much. It facilitates people having different languages. Near about 21% of the total users are from US and 79% of total users are outside of US. A tweet is restricted to a hundred and forty characters; hence it contains such information which is more concise and much valuable. Due to its usage, it is estimated that five hundred million tweets are sent per day by different categories of people including teacher, students, celebrities, officers, musician, etc. So, there is a huge amount of data that is increasing on a daily basis that need to be categorized. The important key feature is to find the keywords in the huge data that is helpful for identifying a twitter for classification. For this purpose, Term Frequency-Inverse Document Frequency (TF-IDF) and Loglikelihood methods are chosen for keywords extracted from the music field and perform a comparative analysis on both results. In the end, relevance is performed from 5 users so that finally we can take a decision to make assumption on the basis of experiments that which method is best. This analysis is much valuable because it gives a more accurate estimation which method’s results are more reliable.

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Nib calligraphy pattern recognition is the way to convert handwritten nib font into its equivalent machine understandable or readable form. Nib calligraphy pattern recognition is derived from pattern recognition and computer vision, a variety of work has been done on Urdu literature and on Urdu handwritten automatic line segmentation. This research work is based on Urdu Nastaleeq Nib calligraphy pattern recognition. The width of the Qalam (Nib) makes difficulties in recognition due to different width of qalam pattern varieties, so there is dire need to develop a system that can recognize the digitized image of Urdu Nastaleeq Nib font with high accuracy. The objective of this research is to create a ground for the development of an efficient and robust Urdu Optical Character Recognition (OCR) for Urdu Nastaleeq nib pattern recognition and to develop a system that can recognize the digitized image of Urdu Nastaleeq Nib font with high accuracy. Urdu Nastaleeq nib pattern recognition. The research work mainly focuses on identifying the Urdu nib calligraphy pattern recognition. The purpose of the research is to create a system for Urdu Nastaleeq Nib calligraphy pattern recognition to get benefit from the cultural heritage of Nib calligraphic material. The Urdu Nastaleeq Nib Calligraphy Pattern Recognition research work is proposed to be done on the calligraphic Urdu Nastaleeq Nib pattern recognition. This research mainly focuses on recognizing the handwritten Urdu Nastaleeq Nib typeset and eliminating the noise which is the main difficulty in interpretation the font clearly. The aim here is to build up a more consistent, correct and precise system for Urdu Nastaleeq Nib calligraphy Pattern Recognition.