The rapid technological advancements in recent years allowed to process different kinds of data to study several real-world phenomena. Within this context, textual data has emerged as a crucial resource in numerous research domains, opening avenues for new research questions and insights. However, many researchers lack the necessary programming skills to effectively analyze textual data, creating a demand for user-friendly text analysis tools. While languages such as R and python provide powerful capabilities, researchers often face constraints in terms of time and resources required to become proficient in these languages. This paper introduces TAll - Text Analysis for All, an R Shiny app that includes a wide set of methodologies specifically tailored for various text analysis tasks. It aims to address the needs of researchers without extensive programming skills, providing a versatile and general-purpose tool for analyzing textual data. With TAll, researchers can leverage a wide range of text analysis techniques without the burden of extensive programming knowledge, enabling them to extract valuable insights from textual data in a more efficient and accessible manner.

TAll: A New Shiny App of Text Analysis for All

Michelangelo Misuraca;
2023-01-01

Abstract

The rapid technological advancements in recent years allowed to process different kinds of data to study several real-world phenomena. Within this context, textual data has emerged as a crucial resource in numerous research domains, opening avenues for new research questions and insights. However, many researchers lack the necessary programming skills to effectively analyze textual data, creating a demand for user-friendly text analysis tools. While languages such as R and python provide powerful capabilities, researchers often face constraints in terms of time and resources required to become proficient in these languages. This paper introduces TAll - Text Analysis for All, an R Shiny app that includes a wide set of methodologies specifically tailored for various text analysis tasks. It aims to address the needs of researchers without extensive programming skills, providing a versatile and general-purpose tool for analyzing textual data. With TAll, researchers can leverage a wide range of text analysis techniques without the burden of extensive programming knowledge, enabling them to extract valuable insights from textual data in a more efficient and accessible manner.
2023
text analysis, shiny app, web app1 1. Introduction In the era of big data, researchers across various disciplines are increasingly faced with the challenge of analyzing vast amounts of textual data. Textual data, such as research articles, social media posts, customer reviews, and survey responses, hold valuable insights that can contribute to the advancement of knowledge in fields ranging from social sciences to healthcare and beyond. Researchers seek to analyze textual data to uncover patterns, identify trends, extract meaningful information, and gain deeper insights into various phenomena. By employing advanced natural language processing (NLP) techniques and machine learning algorithms, researchers can explore the semantic and syntactic structures of texts, perform topic detection, polarity detection, and text summarization among other analyses. Moreover, the advent of digital platforms and the proliferation of online content have generated vast amounts of textual data that were previously inaccessible or challenging to obtain. CLiC-it 2023: 9th Italian Conference on Computational Linguistics, Nov 30 — Dec 02, 2023, Venice, Italy maria.spano@unina.it (M. Spano); massimo.aria@unina.it (M. Aria); corrado.cuccurullo@unicampania.it (C. Cuccurullo); luca.daniello@unina.it (L. D’Aniello); michelangelo.misuraca@unical.it (M. Misuraca) 0000-0002-3103-2342 (M. Spano); 0000-0002-8517-9411 (M. Aria); 0000-0002-7401-8575 (C. Cuccurullo); 0000-0003-1019- 9212 (L. D’Aniello); 0000-0002-8794-966X (M. Misuraca)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/361938
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