نہ کر سانوں تنگ او ماہی
چل اساڈے سنگ او ماہی
تیرے پیار تے جان وکائی
سڑ گئے وانگ پتنگ او ماہی
دل ساڈا پیا دھک دھک ڈولے
چھنکے جس دم ونگ او ماہی
کوئی عاشق، کوئی نفرت کردا
قدرت دے نیں رنگ او ماہی
وچوں ٹھگ تے اتوں بھولا
ویکھو وکھرے رنگ او ماہی
ماہی دِسّے چار چوفیرے
رچیا ہر ہر انگ او ماہی
پیا رقیب تیاریاں کر دا
رنگ دے وچ پا بھنگ او ماہی
Islam is a complete code of life. It has complete instructions regarding all aspects of life. There is no moment in human life when the wisdom of Islam has not benefited humanity. Likewise there is no such area in the world where Islam has not put the light of guidance. Allah Almighty has systematically administered this world system. Allah created the universe and set it disciplined, then created man and gave him every kind of physical, rational, logical and argumintive ability, so that humans can know the secrets of the universe by thinking and understanding it. The journey of research and discovery, which started long ago, is still continues today after passing through different stages of research. This journey of research resulted in evolution of philosophy, civilization, promotion of new scientific innovations and discoveries. The research and discovery resulted in the secrets of heaven and earth. Today, man stands at the stage of science and technology, about which the human being in the older days even could not imagine of it. In this entire evolutionary journey, the religion of Islam is the only religion that not only stood firmly with humans, but guided him at every step during this evolutionary journey. In this context, the Quranic teachings are comprehensive matter. Allamah Sayyuti says "There is no knowledge and problem that is not discussed in the Qur'an. So Islam invites human to research in the system of the universe and presents the system of universe for its authenticity.
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.