TAll: a new Shiny app of Text Analysis for All
Authors: Massimo Aria, Corrado Cuccurullo, Luca D'Aniello, Michelangelo Misuraca, Maria Spano (Università di Napoli Federico II, Università della Campania “Luigi Vanvitelli”, Università della Calabria, K-Synth spin-off, Università di Napoli Federico II, 2023)
Topic: Lexical and Semantic Resources and Analysis

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.

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. Researchers can tap into these resources to explore new research questions, validate existing theories, and generate novel insights. By harnessing the power of computational tools and techniques, researchers can efficiently process and analyze large volumes of text, significantly reducing the time and effort required compared to manual analysis. Moreover, there is a growing recognition of the need for text analysis tools that cater to individuals who may not possess extensive programming skills. While programming languages like R and Python provide powerful capabilities for data analysis, not all researchers have the time or resources to acquire proficiency in these languages. This paper presents the first version of TAll - Text analysis for All - a new R Shiny app that brings together all the major advancements in text analysis developed in recent years. For researchers who lack programming skills, TAll offers a viable solution, providing an intuitive interface that allows researchers to interact with data and perform analyses without the need for extensive programming knowledge. TAll offers a comprehensive workflow for data cleaning, pre-processing, statistical analysis, and visualization of textual data, by combining state-of-the-art text analysis techniques into an R Shiny app.

Conclusion
This paper presented a brief overview of the first version of TAll, a new shiny app for importing, pre-processing, and analyzing textual data. Our idea stems from the now growing need to analyze textual content due to today’s ever-increasing data volume, offering the opportunity to explore it quickly and efficiently, even for those without programming skills. Using a user-friendly text analysis tool, researchers can focus more on their domain expertise and research questions rather than spend significant time learning programming languages or writing complex code. Tools like TAll democratize text analysis, making it accessible to a broader audience and promoting interdisciplinary collaboration. Moreover, general-purpose software can be used in every research field and encourages reproducibility and transparency in research. Paragraphs in every section do not have first-line indent. Use only styles embedded in the document.