مولانا ابوالکلام آزادؔ
بالآخر اس مسیحا نفس نے بھی جان جاں آفریں کے سپرد کردی جو نصف صدی تک اپنے انفاس کرم سے مردہ دلوں میں زندگی کی روح پھونکتا رہا، وہ روشن ضمیر اٹھ گیا جو اپنے نور بصیرت سے تاریک دماغوں کو منور کرتا رہا، کاروان ملت کا وہ حدی خواں رخصت ہوگیا جو اپنی ہدایت و رہنمائی سے گم کردہ راہوں کو راہ راست دکھلاتا رہا، وہ شمع فروزاں خاموش ہوگئی جس کی روشنی سے علم معرفت کا ہرگوشہ منور تھا، ابوالکلام کی وفات تنہا ہندوستان کا نہیں بلکہ پوری دنیائے اسلام کا حادثہ ہے اور اس حادثہ پر جتنا ماتم بھی کیا جائے کم ہے۔
آسماں راحق بودگر خوں ببارد برز میں
ایسی جلیل القدر اور عہد آفریں شخصیتیں مدتوں میں پیدا ہوتی ہیں، جو افکار و تصورات کی دنیا اور قوموں و ملتوں کی زندگی میں انقلاب پیدا کردیتی اور تاریخ کا نیا دور شروع کرتی ہیں اور ترقی و تعمیر کی ہر راہ میں اپنے نقشِ قدم رہنمائی کے لئے چھوڑ جاتی ہیں، حق یہ ہے کہ مولانا کی وفات پر ان کی زبان سے اقبال کا یہ قطعہ آج پھر دہرایا جائے۔
سرور رفتہ باز آید کہ نہ آید
نسیم از حجاز آید کہ نہ آید
سرآمد روزگار ایں فقیرے
وگر دانائے راز آید کہ نہ آید
ان میں فطری عظمت تھی، وہ فلسفیانہ فکر مجتہدانہ دماغ اور مجاہدانہ جوش عمل رکھتے تھے اور اپنے گوناگوں کمالات کے اعتبار سے تنہا ایک عالم تھے، عالم و فن کے امام و مجتہد بھی تھی اور دانائے راز حکیم مفکر بھی، میدان سیاست کے مدبر بھی تھے اور عرصہ جہاد کے شہسوار بھی، سحرطراز ادیب بھی تھے اور جادو بیان خطیب بھی، ذہانت، فہم و فراست، فکر و تدبر کی گہرائی، دیدہ وری و نکتہ رسی میں ان کا کوئی معاصر ان...
The paper deals with the different styles of iltifāt found in the Holy Qur’ān and coming out with a general scheme to account for its occurrence in order to enhance the understanding of the subtleties of this feature of Qur’ān ic style. To accomplish this, the research was carried out by way of an analytical study of the instances of iltifāt in the Holy Qur’ān. As a prelude to the discussion of this subject, the research provides the meaning of iltifāt among the Arab rhetoricians and the status of iltifāt as one of the rhetorical tropes. The paper also discussed the significations of iltifāt which have already been mentioned by previous scholars, and since those scholars touched on the significations only briefly, therefore, the research strived to explore further aspects of their interpretations making an effort to highlight new significations of iltifat and an attempt to introduce a new approach in looking at the iltifāt phenomenon, in different Sura’hs of Qur’ān, to demonstrate the application of this new perspective. Finally the research shows that the occurrence of iltifÉt in the Qur’ān follows certain patterns that are related to the intended significations at the various locations where they appear in the Qur’ān.
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.