Text analysis and text mining are two terms often used interchangeably. Text mining has a research history in the field of information science as a technique to analyze publication to understand the evolution of research trends (Yu et al., 2017). The recent use of text mining also has a linguistics component, with sentiment analysis and text classification to extract topics or genres. Text analysis is a broader term that includes text mining as a process and you can further see how to design a text analysis project with Wachsmuth's (2015) book on this topic.

There are a few web-based text analysis environments that are easy to use. Voyant Tools is one example of a web based tools for quick analysis of small amount of text. TAPoR is a directory of tools for text analysis and retrieval.

Text analysis and text mining described here focus on automatic text processing and analysis, which is different from textual analysis in the context of qualitative research. If you are looking for software applications to support qualitative research data analysis (such as interviews or surveys) that requires human input on tagging text before drawing inferences, see the section on CAQDAS for a list of commonly used applications.

UNT's Data Science and Analytics Services supports students, faculty, and administrators in acquiring data and performing statistical analysis. As stated in their FAQs, they do not provide help with course assignments, and they do not teach basic statistics. However, faculty and graduate students in any department can seek assistance with statistics from the College of Education's Office of Research Consulting.

Although this list is not exhaustive as new tools or packages are being developed to support the growing demand of computer use in qualitative data analysis. The Using Software in Qualitative Research: A Step-by-Step Guide (Silver & Lewins, 2014) provides guidance and consideration when using software for qualitative research including data management, analysis, and preparation of reporting. 

Atlas.ti: An application that can be used with Desktops (Windows & Mac), tablets (iPad and Android), and in the cloud. It was originally developed in Berlin with methodological roots in the grounded theory approach and now expanded to support a diverse scope of qualitative research. The user interface for desktop versions have English, Spanish, German, Portuguese, and Simplified Chinese languages. The ATLAS.ti learning page provides resources for free or paid training, videos, and manuals for users. 

Dedoose: A web-based program to store and analyze qualitative data in text, audio, images or video format. It also allows users to import quantitative data in a spreadsheet format. The Dedoose User Guide and videos are good places to start to learn how to utilize this tool for your research project. 

NVivo: Current UNT staff and students have access to NVivo through UIT's Remote Software Access (formerly "Virtual Lab"). To learn how to use NVivo, see the official NVivo documentation, including video tutorials, and video tutorials available through Linkedin Learning.

For users who are already familiar with R programming or Python, there are packages for each to support qualitative data analysis. RDQA is a R package that works with textual data in plain text format. PyQDA is a Python version of the package that will work with plain text, Microsoft word document, and pdf files. 

While copyright or licensing restrictions will likely prevent you from sharing the collection of documents that you study, you can still share your list of sources, codebook, scripts, and other data that would allow another researcher to replicate your findings. The UNT Libraries can help you: see our information on research data management.