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Topic Modeling of the Pakistani Economy in English Newspapers via Latent Dirichlet Allocation (LDA)

  • COMSATS University Islamabad

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

This research paper explores aspects of the Pakistani economy using the Latent Dirichlet Allocation (LDA) technique. The data based on 3,000 articles were collected from two Pakistani English newspapers, Dawn and The News, (2015–2020), through Lexis Nexis database. The headlines of the news articles relevant to Pakistan’s economy, were taken into account. By employing the data-driven approach of the grounded theory, it is found that changes in policies, security preference, textile industry, the shift of energy, inflation, growth and investment, mega projects, sustainable democracy and poverty control need to be focused to overcome the challenges of Pakistan’s economy. It also reveals that mega projects like the China Pakistan Economic Corridor (CPEC) are called to boost Pakistan’s economy. The results show that smooth trading would help reduce poverty in the country.

Original languageEnglish
JournalSAGE Open
Volume12
Issue number1
DOIs
StatePublished - Mar 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 1 - No Poverty
    SDG 1 No Poverty
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  3. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Keywords

  • data-driven approach
  • grounded theory
  • natural language processing
  • topic modeling

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