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

Fasih Ahmed, Muhammad Nawaz and Aisha Jadoon

SAGE Open, 2022, vol. 12, issue 1, 21582440221079931

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.

Keywords: topic modeling; natural language processing; grounded theory; data-driven approach (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:12:y:2022:i:1:p:21582440221079931

DOI: 10.1177/21582440221079931

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