A Comprehensive Review of the Three Main Topic Modeling Algorithms and Challenges in Albanian Employability Skills

Abstract

Today’s jobseekers face many obstacles while trying to find a career that aligns with their interests, employability soft skills, and professional experience. In Albania, jobseekers frequently initiate their job search by actively exploring job vacancies listed on various online job portals. The analysis of job vacancies posted online provides an added advantage to the labour market actors compared to traditional survey-based analyses. This is because it enables a faster analytical process, promotes decision-making based on accurate data, and should be carefully considered by every country when formulating their Labor Market Policies. Since the data posted online are unlabelled, it has been proven that the potential of unsupervised learning techniques, more precisely the Topic Modelling algorithms, is outstanding when applied to analysing job vacancies, mainly with regard to assessing employability soft skills. Algorithms in topic modelling are essential for uncovering hidden patterns in texts, facilitating the extraction of important data, generating document summaries, and enhancing content comprehension. This paper analyses and compares the three primary methodologies and algorithms used in topic modelling, which can be applied to analyse employability soft-skills: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and BERTopic. At the end of the paper, conclusions are drawn regarding superior performance and optimal algorithm applicability, challenges, and limitations through a review of studies conducted in the Albanian job market.