مولانا سید شاہ محمد قمرالدین پھلواروی
یہ خبر بھی افسوس کے ساتھ سنی جائے گی کہ مولانا سید شاہ محمد قمرالدین صاحب پھلواروی، امیر شریعت صوبہ بہار نے ۳۱؍ جنوری کو انتقال فرمایا، مرحوم مولانا سید شاہ بدرالدین صاحب مرحوم، امیر شریعت اول کے صاحبزادے اور علم و عمل میں اپنے اسلاف کرام کے خلف الصدق تھے، اپنے بڑے بھائی مولانا سید شاہ محی الدین صاحب مرحوم امیر شریعت ثانی کے انتقال کے بعد ان کے جانشین ہوئے اور امارت شرعیہ کی روایات اور اس کے مذہبی کاموں کو پوری طرح قائم و برقرار رکھا، ان کی وفات سے خاندان پھلواروی کی ایک اہم یادگار مٹ گئی، اﷲ تعالیٰ ان کو اپنی رحمت و مغفرت سے سرفراز فرمائے اور ان کے اخلاف کو ان کے نقش قدم پر چلنے کی توفیق بخشے۔ (شاہ معین الدین ندوی، مارچ ۱۹۵۷ء)
The Figures of Speech(ملع عيدبلا )is a Significant branch of Arabic Rhetoric. It has two kinds; Literal Aesthetic, تانسحملا ةيظفللا)) Semantic Aesthetic, (تانسحملا ةيظفللا). Both kinds are having a pivotal role in the miracle of Qurān. The Great Scholar of Rhetoric Al-Zamakhshari has mentioned many of its types to analyze the Qurānic Verses rhetorically in his exegesis Al-Kashāf. The Great Scholar Abd Al-Qāhir Al-Jurjāni did not approach the upper mentioned kinds, not for the reason of non-interference in The Qurānic miracles but he was always eager to derive new ideas in this particular field. As it is known that many former scholars have approached all kinds of the Figures of Speech in a wide range and Abd Al-Q┐hir Al-Jurjāni was dominated by his creative nature. In this article, it has been discussed widely the academic ambivalence surrounding Abd Al-Q┐hir Al-Jurjāni's lack of interest in the Figures of Speech among three modern scholars: Dr. Muhammad Ahmad Al-ķwfi, Dr. Muhammad Shwq┘ Zaif, Dr. Muhammad Ab┴ Mosā.
Sentiment Analysis is currently one of the most studied research fields. Its aim is to analyze people‟s sentiments, opinions, attitudes etc., towards different elements such as topics, products, individuals, organizations, and services. Sentiment analysis can be achieved by machine learning or lexical based methodologies or a combination of both. Recent research shows that domain specific lexicons perform better as compared to domain independent lexicons. In an effort to improve the performance of domain independent lexicons, this research incorporates machine learning with a lexical based approach, introducing a new approach called SWIMS, to determine the feature weight based on a well-known general-purpose sentiment lexicon, SentiWordNet. Support vector machine is used to learn the feature weights and an intelligent model selection approach is employed in SWIMS in order to enhance the classification performance. The features are selected based on their subjectivity and the effects of feature selection with respect to their part of speech information are studied extensively. Seven benchmark datasets have been used in this research, including large movie review dataset, multi-domain sentiment dataset and Cornell movie review dataset, all of which are freely available online for research purposes. In-depth performance comparison is conducted with the state of the art machine learning approaches, lexical based methodologies, and other tools used for sentiment detection. The proposed SWIMS approach attained accuracy, precision, recall and f-measure values of 83.06%, 83.30%, 82.83% and 83.03% respectively, averaged over the seven datasets. The evaluation of performance measures proves that the proposed approach outperforms other techniques for sentiment analysis.