In this research the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by using the Lexicon-based approach. In the lexicon, apart from the traditional approach that considers adjectives only, nouns and verbs are also included. An efficient Urdu Sentiment Analyzer is developed that applies rules and makes use of this new lexicon to perform Urdu Sentiment Analysis by classifying sentences as positive, negative or neutral. Negations, intensifiers and context-depentent words are effectively handled for enhancing accuracy of Urdu Sentiment Analyzer. Specific rules for handling negations, intensifiers and context-dependent words are incorporated in Urdu Sentiment Analyzer. For testing the Lexicon-based approach, a corpus of 6025 sentences from 151 blogs belonging to 14 different genres is collected and the sentences are annotated by three human annotators to classify each sentence as positive, negative and neutral. Evaluating this Urdu Sentiment Analyzer, by using sentences from the corpus, yields the most promising results so far in Urdu language (up to the knowledge of the author) with 89.03% accuracy, 0.86 precision, 0.90 recall and 0.88 f-measure. The comparison with the previous works in Urdu Sentiment Analysis shows that the combination of this Urdu Sentiment Lexicon and Urdu Sentiment Analyzer is much more effective than the previous such combinations. The main reason for increased efficiency is the development of wide coverage lexicon and effective handling of negations, intensifiers and context-dependent words by the Urdu Sentiment Analyzer. Although high accuracy is achieved by Lexicon-based approach in multiple domains for Urdu Sentiment Analysis, which is the main objective of this research, but for comparison, Supervised Machine Learning approach is also used. Three well known classifiers that are Support Vector Machine, Decision Tree and K Nearest Neighbor are tested; their outputs are compared and their results are ultimately improved in several iterations. It is further concluded that K Nearest Neighbor is performing better than Support Vector Machine and Decision Tree. For verification of this result, three evaluation measures i.e. McNemar’s Test, Kappa Statistic and Root Mean Squared Error are used. The result from all these three evaluation measures confirmed that K Nearest Neighbor is performing much better than the other two classifiers and achieved 67.02% accuracy, 0.68, 0.67 and 0.67 precision, recall and f-measure respectively. The results from both the approaches are compared. On the basis of experiments performed in this research, it is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach, when Urdu Sentiment Analysis is performed in multiple domains in terms of accuracy, precision, recall and f-measure, economy of time and effort.
مولوی سید ممتاز علی اخبارات کی زبانی ناظرین تک یہ خبر پہنچی ہوگی کہ ہماری زبان کے پرانے ادیب و مصنف اور مسلمان لڑکیوں کی تعلیم و تربیت کے مشہور مبلغ مولوی سید ممتاز علی صاحب مہتمم تہذیب نسواں لاہور نے وفات پائی۔ انَّاﷲ، مرحوم کا وطن، دیوبند تھا، عربی کی تعلیم پائی تھی، اور ساتھ ہی جدید تعلیم سے بھی بہرور تھے، اس زمانہ میں سرسید مرحوم کی تحریک کا شباب تھا، اس شمع کے گرد جو پروانے جمع ہوگئے تھے، ان میں ایک یہ بھی تھے، مسلمانوں میں وہ پہلے شخص تھے جس نے تعلیم نسواں کی تبلیغ کی، اور اس تبلیغ میں ان کو کامیابی حاصل ہوئی تھی، وفات کے وقت ان کا سن ستر (۷۰) کے قریب ہوگا ابھی اسی سال کے اخیر جنوری میں لاہور میں ملاقات ہوئی تھی، اسی وقت وہ زار و نزار، اور حبس بول کی شکایت میں مبتلا تھے، آخر وہ اس تکلیف سے جانبر نہ ہوسکے اور چل بسے، اﷲ تعالیٰ ان پر رحمت فرمائے، نہایت خوش خلق متواضع اور مرنج و مرنجان بزرگ تھے۔ (سید سلیمان ندوی،اگست ۱۹۳۵ء)
Islam is a complete religion which provides guidance not only for spiritual life but about material aspect of life also. There is a clear code of business ethics to be followed in trade. Unfortunately Islamic ethics are being ignored in our society. In this article, reasons of our current materialistic unethical point of view in trade and some suggestions to resolve the problem are being discussed
The ERP solutions contribute in making financial performance of companies in a long term period. The
demands of such systems are mostly raised by manufacturing companies in our country. The top management
support, implementation team support and end user training & support during implementation of ERP systems
directly and indirectly effect returns of investments of the companies. Initially success of ERP solutions is
dependent upon these three factors. The management, team and users focus on profitability of the company.
In this study the accounts & finance departments of production companies of Faisalabad are selected where
ERP systems have been implemented. The sample size constitute on three hundred respondents that are
selected from accounts & finance offices of companies. The gross profits, net profits, equity capital, and all other
expenditures & revenues are managed in database of ERP finance module. Our results show that ERP finance
module positively associated with ROI of company. The top management support, ERP implementation team
support and user support & training positively contribute in success of ERP systems and positively affect ROI
of company. Firstly the top management is more conscious to increase the profitability of company by focusing
on suitable ERP packages. For this purpose performance and support of ERP implementation teams is required
but these teams concentrate less on ROI of the companies. The ERP system users efficiently control financial
data on databases if sufficient training provide to them. The end users highly concentrate on profitability of the
companies.