arXiv — NLP / Computation & Language · · 3 min read

Cortex: Content Analysis Support Software, a Resource for Qualitative Research

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Computer Science > Digital Libraries

arXiv:2609.11970 (cs)
[Submitted on 29 Aug 2026]

Title:Cortex: Content Analysis Support Software, a Resource for Qualitative Research

View a PDF of the paper titled Cortex: Content Analysis Support Software, a Resource for Qualitative Research, by Ana Julia da Silva Soares and Rafael Coimbra Pinto
View PDF HTML (experimental)
Abstract:Qualitative research is widely used in the human and social sciences, characterized by a deep understanding of phenomena through the interpretation of meanings and contexts. Among qualitative data analysis methods, content analysis stands out as a consolidated technique, which allows for the systematic description and interpretation of textual contents. However, as data volume increases, the time required for organization, reading, and categorization becomes a significant challenge, potentially delaying research development. Therefore, this work aimed to develop a web application to support content analysis, based on Bardin's methodology, targeted at academic researchers. The methodology adopted a mixed approach, combining bibliographic research on content analysis with semi-structured interviews with four experienced researchers, aiming to identify real needs and requirements. Based on these inputs, the Cortex software was developed to assist the researcher in the pre-analysis and material exploration stages, generating suggestions for indices, indicators, and categories with full traceability to original documents. The results demonstrate that Cortex is capable of guiding the researcher through the entire methodological workflow, from corpus configuration to results exportation, acting as a methodological collaborator without replacing the researcher's interpretative autonomy.
Comments: 27 pages, 3 figures
Subjects: Digital Libraries (cs.DL); Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2609.11970 [cs.DL]
  (or arXiv:2609.11970v1 [cs.DL] for this version)
  https://doi.org/10.48550/arXiv.2609.11970
arXiv-issued DOI via DataCite

Submission history

From: Rafael Pinto [view email]
[v1] Sat, 29 Aug 2026 18:06:43 UTC (2,591 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Cortex: Content Analysis Support Software, a Resource for Qualitative Research, by Ana Julia da Silva Soares and Rafael Coimbra Pinto
  • View PDF
  • HTML (experimental)
  • TeX Source

Current browse context:

cs.DL
< prev   |   next >
Change to browse by:

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language