Nawaz Sharif’s Speeches to the United Nations General Assembly: A Corpus-Based Analysis

Every year heads of different states make their speeches in the general debate of United Nations General Assembly (UNGA). They address national and international issues and suggest measures to resolve them with the help of international community and the United Nations. Muhammad Nawaz Sharif also made speeches in the UNGA. In 2013, he was newly elected prime minister and was enthusiastic to resolve issues with India. There was no internal political pressure on him at that time and the government in India was hostile towards Pakistan. But one year in power and embattled with domestic issues and international pressure, the prime minster approached the issues differently in his 2014 speech. The aim of this paper is to compare his two speeches and see what changes occurred in his stance towards India. The main focus of the analysis is: Who is the addressee of these speeches? How confident did Nawaz feel about the solution of the issues that he raised. The Corpus tool Wmatrix has been used to analyze the data. The results show significant differences in his two speeches with regard to his stance towards India.

Authorship Attribution for Urdu Newspapers Columns Using Text Mining Techniques

With emergence of big data analytics in last decade, the importance of analyzing semistructured and unstructured data (such as text) is also highlighted. Since, the text (such as customer reviews, newspaper articles, etc.) contain significant business information, the text analytics becomes more significant to predict, infer or analyse information to add value to the business. In this research, we present a unified approach for intelligent association analysis of text that how much a piece of text is related to a customer or a person In this dissertation, an approach is presented for Authorship attribution in Urdu text using LDA model with n-grams texts of authors and improved sqrt-cosine similarity for the sake of forensic analysis. The proposed approach uses n-grams words to identify various learned representations of stylometric features and use them to identify the writing style of a particular author. The LDA based approach emphasizes instance-based and profile-based classification of an author’s text. Here, LDA suitably handles high dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing styles of authors, and the inherent ambiguity of the Urdu language. A large corpus has been collected for performance testing of the presented approach. The results of experiments show superiority of the proposed approach over the state-of-the-art representations and other algorithms used for Authorship attribution. Manifold contributions of the presented work are use of improved sqrt-cosine similarity with LDA topics to measure similarity in vectors of text documents for the forensic analysis purpose, construction of a large data set of 6000 documents of columns, and achievement of 92% results on Urdu columns with fifteen authors and 78.57% results on PAN12 English dataset with fourteen authors without using any labels for authorship attribution task.