المنھج اللغوي في التفسير و تاريخه

The Holy Quran was revealed in Arabic Language, it is, therefore necessary to seek Arabic Diction to gain the direct guidance from it. The companions of Holy Prophetr, Tabeen, and the reverent Imams strictly rebuked those interpreters who interpret the Holy Quran without having command over Arabic Language. The verses of Quran that are clear in comprehension, explicit and easy, do require the source of interpretation as “Arabic Diction”. This method highlights the positive trends to Arabic Diction. But in the matter of ambiguity and resemblance in verses and deduction of Masael, this Diction will be given second priority. Mere Diction and Arabic Socio-Diction may not be titled as most authentic. Diction is not the ‘last word. ’ The very first priority will be given to the verses of Quran, Hadith e Nabvi and Quotations of Companions of Holy Prophetr. The companions themselves were the native Arabs but they used to do consult some Quranic terms with the Holy Prophetr. As time passed, some strayed sects and atheists ignored this positive trend (Tafseer-bil-Mathur), and accustomed a new trend of interpretation of Holy Quran i. E. Depending upon Arabic Diction only so that they may endorse their own thoughts. It was a negative source of interpreting the Holy Quran i. E. Only by Arabic Diction. The present article explores its historical perspectives after evaluating its negative trends. The Motazila sect got this trend nourished. The representing interpretations of Holy Quran of this trend have been analyzed in this article. At the end, Molana Ameen Ahsan Islahi’s approach to Diction and his Tafseer ‘Tadabbur e Quran’ has been evaluated.

A Framework to Improve Classification of Positive and Negative Opinions in Roman Urdu-English Code Switching Environment

In computational linguistics, sentiment analysis facilitates classification of opinion as a positive or a negative class.In last decade, the area of sentiment analysis of English language is explored largely with different techniques those have improved the overall performance.Urdu is language of sixty-six million people and largely spoken in south-asian subcontinent. Also, it is national language of Pakistan which is world sixth most populous country according to United Nations Population Division. Sentiment analysis of Urdu language is important tool to understand the behavioural aspects, cultural values and social habits of the people living in this part of world. Opinion mining is also crucial for governments, policy makers, business owners and brand ambassadors to make their decisions in accordance to sentiment of the public.However, sentiment analysis of Urdu language is not well explored as that of English language. The Urdu sentiment analysis is performed with simple Bag-of-Word (BoW) method and machine learning (ML) techniques with limited set of features. The BoW method is not sufficient to handle complex opinions. Also, the accuracy of ML techniques, with legacy features, is not comparable to the sentiment classification task of other languages. For English language, the discourse information (sub-sentence level information) boosted the performance of both BoW method and ML techniques. A theory for Urdu sentiment analysis that extract and use the discourse information at sub sentence level and also suggest a computational model to achieve more accurate and better results than the simple bag of word approach. The proposed solution segmented the sentiment into two sub-opinions, extracted discourse information (discourse relation and polarity relation), proposed an extended BoW method (rule based method) and suggested a new small subset of features for ML techniques. The results significantly enhance (p < 0.001) the performance of recall, precision and accuracy by 37.25%, 8.46%, and 24.75% respectively. The current research targeted sentiment with two sub-opinions that remain excellent until the opinions are short messages like those on Twitter, in forum comments or as Facebook status posts. The proposed technique can be extended for sentiments with more than two sub-opinions such as blogs, reviews, and TV talk shows.