میاں بیوی میں سے ایک کا قبول اسلام

In the early days of Islam, either of the married couple entering into Islam does not affect their marriage contract and it continued to be valid even after the Emigration of the Holy Prophet (peace and mercy be upon him) to Madina. In fact, it has been observed until Hudhabiya Truce was signed. It was also included the terms if someone from Quraish without accompanying the wali approaches the Holy Prophet (Peaceand Mercy be upon him), he/she will be returned to Makkah. After this agreement, many women came to Madina and embraced Islam. But their spouses and relatives followed them their way to Madina. They claimed them back to Makkah. In this respect, Allah almighty revealed a verse of Surah Mumtahina, which is an express evidence that such believing women must not be returned to their former infidel husbands. They were commanded so to marry believing husbands after their separation from their disbelieving spouses. Through this verse, Muslim husbands were forbidden to stay and have conjugal relations with their nonbelieving wives, too. Similarly,  every believing wife was forbidden to reside with her disbelieving husband. The companions abided by the ruling in its entirety and separated from their non- believing spouses. Jurists have derived many instructions from the verse 10 of Surah Mumtahina, which are discussed in this paper.

A Robust Framework of Sentiment Analysis for Online Customer Reviews and Blogs

The Web 2.0 has dramatically changed people‟s communication style. It is a great move toward more community oriented, highly collaborative, interactive and responsive Web. Today we are not only using the Internet but we are part of this global network. Social media sites became the world‟s largest virtual community, where people express their views about products, events and services, anytime from anywhere. These views have great impact on community thinking and decisions. The most flourished feature of this era is the rising of blogging which provides resourceful and open way to anyone, anywhere. These data sources provide the rich basis for sentiment analysis. The statistics show that 80% of consumers have changed their decisions about purchase based on negative reviews found online. The study found that blogs are 63% more likely to influence purchase decisions than magazines. Evaluation of social media has powered interest in sentiment analysis. There exist two main approaches for extracting sentiment automatically, the lexicon-based approach and statistical or machine learning approach. The later approach demands a lot of training data to learn lexical items that express sentiment and its performance drops when the same classifiers is used in a different domain. The main focus of this work is to develop a lexicon-based framework for automatic classification of blogs and reviews with respect to their semantic orientation. This method consists of three major components: Sentiment analysis, Slang‟s detection and scoring, and Context-aware spelling corrector. Lexicon-based methods for sentiment analysis are robust, give good performance in cross-domain and can be easily boosted with additional source of knowledge. It performs well on blog posting, reviews and also a preferable classifier for handling contextual valence shifters. Irrespective of these merits no single lexicon can perform in an optimal way all the time. This method uses a dynamic, updateable and comprehensive lexicon based on existing opinion lexicons, dictionaries and other machine-readable resources to classify the user-generated contents into positive, negative and neutral polarity. vii Slangs and spelling correction are two vital elements for sentiment analysis because slang and misspelled word may affect the sentiment score. These two issues were handled using Web resources and Statistical language model. The proposed work was implemented, and evaluated with different datasets of reviews and blogs. The empirical results show that the proposed work outperforms the existing, related methods and achieves 90.3% accuracy on average. This method showed high accuracy in binary classification. All the three components of the proposed method performed well with different domains.