Topic Evolution and Emerging Topic Analysis Based on Open Source Software

Bibliometric approach to inclusive entrepreneurship: what has been written in scientific academia?

Purpose

The year 2020 was heavily marked by an unprecedented pandemic affecting society as a whole. However, under-represented groups may have seen their financial and social situation affected differently from other groups. Thus, it was found that in the literature, the term inclusive entrepreneurship, which addresses these issues, was fragmented in view of its similarity and association with social entrepreneurship, inclusive business and sustainability. In this sense, this paper aims to map the scientific knowledge on this topic.

Design/methodology/approach

To fulfil this aim, a systematic literature review was supported by bibliometrics (performance analysis and scientific mapping) and by the use of the software Bibliometrix R and VoSviewer.

Findings

The results obtained show that in the Web of Science, there are 121 documents related to this topic whose content analysis revealed that they are distributed between sustainability, entrepreneurship and inclusive entrepreneurship in the close triple association.

Practical implications

The main contributions of this study are the connection established between the three concepts and the emergence of continuing to develop research on inclusive entrepreneurship, given its binary function: employment generation for disadvantaged groups and inclusive business creation.

Originality/value

The relevance of this bibliometric analysis stands out, providing the positioning of academics on the importance of leveraging emerging research on this topic, not only in poor countries but also in others.

Emerging Research Topic Detection Using Filtered-LDA

Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic.