Open Source

Software & Tools

TALL

Text Analysis for ALL

R Package Shiny Web App 3,000+ CRAN installs MIT License

TALL is an interactive R package and Shiny web application for accessible quantitative and qualitative text analysis, from topic modeling to social media mining, without requiring any coding skills.

Co-authored with M. Aria, M. Spano, C. Cuccurullo, and C. Misuraca, TALL was officially released on CRAN in February 2025. It offers a user-friendly graphical interface for exploring, modeling, and visualizing textual data, incorporating the most recent methods proposed in the literature. With over 3,000 installations, it has attracted growing interest in the scientific community for accessible, robust, and transparent text analysis solutions.

TALL supports a wide range of analytical tasks: frequency analysis, sentiment analysis, topic modeling (LDA, STM), thematic maps, network analysis of co-occurrences, and social media analytics. It is designed for researchers, students, and professionals who need to perform rigorous text analysis without programming expertise.

Text Exploration

Frequency analysis, word clouds, n-gram analysis, and corpus statistics

Topic Modeling

LDA and STM topic models with interactive visualization and parameter tuning

Semantic Networks

Co-occurrence networks and thematic clustering of textual data

Social Media Mining

Import and analyse social media data from multiple platforms

Visit tall-app.com Read the paper (SoftwareX 2026)

bibliometrix

Comprehensive Scientometric Analysis in R

R Package Adopted worldwide Open Source

bibliometrix is a comprehensive open-source R tool for quantitative research in scientometrics and bibliometrics, including performance analysis and science mapping. It is one of the most widely used tools for scientometric analysis globally.

Originally developed by M. Aria & C. Cuccurullo (2017), bibliometrix provides a framework for bibliometric data management and analysis. I am a member of the core development team since 2020, contributing to the expansion of its analytical capabilities and the development of biblioshiny, its interactive Shiny interface.

bibliometrix and its Shiny counterpart, Biblioshiny, are used by researchers in more than 150 countries. They support systematic literature reviews, co-citation analysis, keyword co-occurrence analysis, historiographic analysis, thematic mapping, and science mapping at a global scale.

Science Mapping

Keyword co-occurrence, co-citation, and bibliographic coupling analysis

Performance Analysis

Productivity and impact indicators for authors, journals, institutions, and countries

Systematic Reviews

Support for PRISMA-compliant systematic literature reviews with automated tools

Biblioshiny Interface

Interactive no-code Shiny web app for visual bibliometric analysis

Visit bibliometrix.org

themescopeR

Social Representation Analysis via Semantic Network Mapping

R Package Shiny GUI Open Source MIT License

themescopeR detects and visualises social representations in large-scale digital text corpora by building sentence-level word co-occurrence networks, identifying thematic communities, and mapping them onto a strategic diagram grounded in Social Representation Theory.

The package implements the ThemeScope method (Misuraca, Spano & D'Aniello, Journal of Information Science), transforming raw documents into interpretable thematic structures through a transparent pipeline, from linguistic annotation and vocabulary building to co-occurrence analysis, community detection, and visualisation. It is designed for researchers who need a reproducible, quantitative way to explore how meaning circulates in online discourse.

Two indicators drawn from Social Representation Theory position each theme on the strategic diagram: the Prototypical Salience Index (PSI), capturing structural centrality and frequency (anchoring), and the Concreteness Score (CS), quantifying the use of concrete, imageable vocabulary (objectification).

Co-occurrence Networks

Sentence-level term associations built into thresholded semantic networks

Community Detection

Thematic communities via walktrap, Louvain, or Leiden algorithms

SRT Indicators

Prototypical Salience Index (PSI) and Concreteness Score (CS) on a strategic diagram

Console & Shiny GUI

End-to-end analysis from the R console or an optional no-code Shiny interface

View on GitHub Read the paper (J. of Information Science)