Zuberi, Mahnaz Hassan
Institute for Educational Development, Karachi
MEd
Aga Khan University
Private
Karachi
Sindh
Pakistan
2006
Completed
Education
English
2021-02-17 19:49:13
2024-03-24 20:25:49
1676727885385
غزلیات
زندہ رکھی ہے ترے پیار کی خوشبو ایسے
اپنے شعروں میں دیے تیرے حوالے میں نے
کیوں نہ ہر لفظ پہ آنکھوں سے یہ ٹپکیں آنسو
جس قدر درد ملے شعر میں ڈھالے میں نے
ز
اپنی ذات کے اندر آ!
خواب نگر سے باہر آ!
شیش محل میں کس نے کہا
ہاتھ میں لے کر پتھر آ!
دیکھ کے صحرا نے یہ کہا
آنکھ میں بھر کے ساگر آ!
تجھ سے مسخر دل نہ ہو گا
لاکھ تو بن کے سکندر آ!
ہنس کے کہا درویش نے یہ
غم کی آگ میں جل کر آ!
ز
یوں تو کہنے کو فقط صبح کا تارا ڈوبا
ہاں، مگر رات کے راہی کا سہارا ڈوبا
جس کی باتوں سے سدا پیار کی کرنیں پھوٹیں
اُس کی آنکھوں میں مری سوچ کا دھارا ڈوبا
میری قسمت بھی ہے روٹھی مرے ساجن کی طرح
میں جو منجدھار سے نکلا تو کنارا ڈوبا
آج پھر سُرخ گھٹائوں کی ہے آمد آمد
آج پھر ۱ور کوئی درد کا مارا ڈوبا
کیسا کہرام مچا ہے لبِ دریا یونسؔ
ایسا لگتا ہے کوئی جان سے پیارا ڈوبا
ز
مل کر جو چلا جاتا
کیا اس میں ترا جاتا
معیار سے اپنے تو
کیوں کر ہے گرا جاتا
منزل نہ بھلے ملتی
رستہ تو دیا جاتا
ہوتی ہے وہاں غیبت
میں کیسے بھلا جاتا
ز
اب خونِ جگر دے کے یہ پالا نہیں جاتا
آ جا کہ ترا درد، سنبھالا نہیں جاتا
ہم جس کے سبب نقل مکانی پہ تلے ہیں
کیوں شخص وہ بستی سے نکالا نہیں جاتا
روتے ہو یہ بتلائو کہ تم ہجر میں کس کے
بے وجہ تو آنکھوں کا اُجالا نہیں جاتاHistorical Roots of Radicalization in Pashtun’s Society
This research article aims to trace the history of radical movements in the North-West frontier of sub-continent. Historically, radical movements have long roots in Pakhtun Society. People recruited in different epochs from Pakhtun society branch into various freedom movements before the partition of sub-continent. Freedom movements against the Sikh, Hindu and the British lifted radical impact on Pakhtun Society before the partition of sub-continent. Radical movements after the partition of sub-continent also established their roots in the North-West region of Pakistan. These radical movements engineered the pluralistic cultural values of Pakhtun Society. These movements have lifted radical trends in the North-West frontier of sub-continent. Pakhtuns and their cultural values were not only exposed to violence but the evolution of their culture had been disturbed. N Enhanced Framework for Sentiment Analysis of Social Media Contents Using Supervised Learning Techniques
Sentiment analysis or opinion mining is proven to be very effective to analyze huge and complex
amount of text of social media. Social media provides an online environment for the users to
show their behaviors and emotions through tweets and post. Massive amount of personal
information is placed on the World Wide Web due to huge usage of social media. Moods and
emotions of the user are different from each other. Sentiment analysis of any written text
especially social media content is applicable to extract the opinions, emotions and meaningful
insights for better decision making. There are many challenges in the accurate and reliable
sentiment analysis of available social media content. The challenges can be both technical and
theoretical. Machine learning-based sentiment analysis techniques have issues such as huge
lexicon, semantic gap, handling of negation, domain dependency and bi-polar words. Previously,
many machine learning and data mining techniques have been proposed by several researchers to
resolve these issues. However, the existing techniques have failed to provide satisfactory and
reliable results for most of the available datasets.
A novel methodology is proposed to overcome above mentioned issues using better and
simplified way with less computational complexity and high reliability. Data acquisition, feature
encoding, data preprocessing, feature selection, and classification are the various phases of
implemented framework. Data gathering and preprocessing step is very critical in the analysis of
data. The proposed research mainly contributes during data preprocessing, feature encoding, and
classification phases. In feature encoding phase, a hybrid approach of bi-gram and tri-gram is
used for word embedding. In the experiments, several benchmark datasets have been utilized to
evaluate the effectiveness of the proposed framework. The results obtained from the proposed
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methodology show better or at least comparable results with maximum confidence. The outcome
of the proposed work will be helpful to enhance the process of sentiment analysis of social media
contents. The experimental results of the framework will be validated using WEKA simulation
software.