28. Al-Qasas/The Narratives
I/We begin by the Blessed Name of Allah
The Immensely Merciful to all, The Infinitely Compassionate to everyone.
28:01
a. Ta. Sin. Mim.
28:02
a. These are the Messages of the Clear Book - The Divine Qur’an.
28:03
a. WE are going to recount to you some of the narratives of Moses and Pharaoh truthfully, for a people who believe.
28:04
a. Indeed, Pharaoh exalted himself in the land of Egypt, and
b. divided its citizens into different ethnic and social factions,
c. seeking to oppress one faction among them - Descendants of Jacob – and depriving them of all human rights and civil liberties,
d. and, slaughtering their baby-boys at birth, while sparing their women/baby-girls.
e. He - Pharaoh - was truly of the oppressors and tyrants.
28:05
a. However, WE wanted to empower those very people who were being oppressed in the land
of Egypt – Descendants of Jacob, and
b. to make them the leaders and to make them the inheritors.
28:06
a. And to empower them in the land by giving them political power and religious authority,
b. and to show Pharaoh and Haman and their armies - through them - the very thing that they had dreaded - and trying to prevent.
28:07
a. And so when Moses was born, WE inspired Moses’ mother by saying that:
b. ‘Keep breast feeding him as usual, but when you fear for his life, then put him afloat into the River Nile,
c. and, once you have done so, then do not fear and do not grieve for he will be saved.
d. Surely WE...
‘A great man’, says Justice Oliver Wendell, Jr, ‘represents a great ganglion in the nerves of society, or to vary the figure, a strategic point in the campaign of history, and part of his greatness consists in being there’. (italic ours). And Maulana Muhammad Ali was one such nerve-centre in Indo-Muslim society during the second and third decades of the twentieth century. Indeed, he was one such strategic point in the onward march of Indo-Muslim politics that eventually found culmination and crystallization in the emergence of Pakistan. Actually no one else represented the tone, tenor and temper of the romanticist, Khilafatist era (in the 1910s and 1920s) as he did in his hectic life, his revolutionary activities his numerous discomfitures, and in his tragic death. Whether he led a hectic life, whether he took recourse to a revolutionary path, or whether he goaded himself to die a tragic death outside the frontiers of his motherland cataclysmically, in whatever he did, he, consciously or unconsciously, carried forward the campaign of Indo-Muslim history: the redemption of Islam in India and abroad. In other words, he stood, above all, for an honourable existence for Muslims in India and in the rest of the troubled Muslim world in the existential crisis that convulsed Muslim India and that world.
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.