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جگرؔ مرادآبادی

جگرؔ مراد آبادی
افسوس ہے کہ بزم شاعری کی وہ شمع جو ایک عرصہ سے جھلملا رہی تھی، بالآخر خاموش ہوگئی، جناب جگرؔ مراد آبادی نے ۹؍ ستمبر کو گونڈہ میں انتقال کیا، وہ صحیح معنوں میں اس دور کے رئیس المتغزلین تھے، غزل مدتوں سے جسم بے جان ہورہی تھی، سب سے پہلے حسرتؔ کی مسیحائی نے اس میں جان ڈالی پھر فانیؔ، اصغرؔ اور جگرؔ نے اس کو سنوارا، یہ چاروں غزل کے ارکان اربعہ تھے، لیکن جگر نے اس کا رنگ ایسا نکھارا کہ ان کا طرز تعزل غزل گوئی کا معیار قرار پایا، انھوں نے تغزل کو اس درجہ تک پہنچادیا ہے کہ مستقبل قریب میں ان کے جیسا غزل گو پیدا ہونے کی امید نہیں، ان کا طرز اس قدر مقبول ہوا کہ نئے شعراء کی پوری نسل اس سے متاثر ہوئی اور نہ صرف تغزل بلکہ جگر کے ترنم، وضع قطع حتی کہ شاعرانہ لاابالی پن کی بھی تقلید کی جانے لگی اردو شاعری کی تاریخ میں کسی شاعر کو اپنی زندگی میں شائد ہی اتنی مقبولیت حاصل ہوئی ہو اور اس کا اتنا ہمہ گیر اثر پڑا ہو۔
اخلاقی حیثیت سے بھی جگر اتنے شریف، وضعدار، بلند نظر اور عالی ظرف انسان تھے کہ اس دور کے شاعروں میں اس کی مثال ملنا مشکل ہے، اعظم گڑھ اور دارالمصنفین سے ان کا تعلق بہت قدیم تھا، ان کا تعارف یہیں سے ہوا اور ان کی شہرت نے یہیں سے پرپرواز نکالے آج سے پینتیس سال پہلے جب وہ چشمہ کے تاجر کی حیثیت سے اعظم گڑھ آتے تھے اس وقت مرزا احسان احمد صاحب نے ۱۹۱۹؁ء میں مخزن میں پہلی مرتبہ ان کا تعارف کرایا، پھر ۱۹۲۱؁ء میں ان کا پہلا دیوان ’’داغ جگر‘‘ اپنے مقدمہ کے ساتھ شائع کیا، یہ مجموعہ معارف پریس میں چھپا تھا، اسی زمانہ سے...

Proposing Sociological Research on Children Health Problems in Pakistan

Like many low-income countries, Pakistan is facing children’s health problems. The major health problems affecting children in the country are Pneumonia, Diarrhoea, Measles, Malaria and malnutrition. There is much research has already been conducted on biomedical and epidemiological aspects of these health problems, but little is known about the social and cultural dimensions of children’s health issues. This paper attempts to propose the sociological research on children’s health problems in Pakistan with the emic focus on local context. The proposed future research may mainly be situated in the interpretivist paradigm of qualitative inquiry. Thus, it will contribute in up-scaling the very basic understanding of the meaning formed by people about social determinants of prevailing children health problems and their potential hazardous consequences in Pakistan.

Enhancing Accuracy of Urdu Sentiments Analysis, Using Lexicon-Based Approach

In this research the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by using the Lexicon-based approach. In the lexicon, apart from the traditional approach that considers adjectives only, nouns and verbs are also included. An efficient Urdu Sentiment Analyzer is developed that applies rules and makes use of this new lexicon to perform Urdu Sentiment Analysis by classifying sentences as positive, negative or neutral. Negations, intensifiers and context-depentent words are effectively handled for enhancing accuracy of Urdu Sentiment Analyzer. Specific rules for handling negations, intensifiers and context-dependent words are incorporated in Urdu Sentiment Analyzer. For testing the Lexicon-based approach, a corpus of 6025 sentences from 151 blogs belonging to 14 different genres is collected and the sentences are annotated by three human annotators to classify each sentence as positive, negative and neutral. Evaluating this Urdu Sentiment Analyzer, by using sentences from the corpus, yields the most promising results so far in Urdu language (up to the knowledge of the author) with 89.03% accuracy, 0.86 precision, 0.90 recall and 0.88 f-measure. The comparison with the previous works in Urdu Sentiment Analysis shows that the combination of this Urdu Sentiment Lexicon and Urdu Sentiment Analyzer is much more effective than the previous such combinations. The main reason for increased efficiency is the development of wide coverage lexicon and effective handling of negations, intensifiers and context-dependent words by the Urdu Sentiment Analyzer. Although high accuracy is achieved by Lexicon-based approach in multiple domains for Urdu Sentiment Analysis, which is the main objective of this research, but for comparison, Supervised Machine Learning approach is also used. Three well known classifiers that are Support Vector Machine, Decision Tree and K Nearest Neighbor are tested; their outputs are compared and their results are ultimately improved in several iterations. It is further concluded that K Nearest Neighbor is performing better than Support Vector Machine and Decision Tree. For verification of this result, three evaluation measures i.e. McNemar’s Test, Kappa Statistic and Root Mean Squared Error are used. The result from all these three evaluation measures confirmed that K Nearest Neighbor is performing much better than the other two classifiers and achieved 67.02% accuracy, 0.68, 0.67 and 0.67 precision, recall and f-measure respectively. The results from both the approaches are compared. On the basis of experiments performed in this research, it is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach, when Urdu Sentiment Analysis is performed in multiple domains in terms of accuracy, precision, recall and f-measure, economy of time and effort.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

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