What is Textual Analytics?
Textual or Text Analytics are any analysis of text-based or unstructured data for the purposes of understanding deeper information in those data. The process often requires capturing, building and cleaning large datasets comprised of text, and designing inference models based upon those processes. Business use these data analytics all the time given their focus upon text for basic communication, not to mention the advanced needs of text-based reporting for organizational activities such as decision-making, report archiving, stakeholder assessment, and message crafting.
What Technology is Used?
- Cloud Services
- Cloud computing is the delivery of computing services -- including servers, storage, databases, networking, software, analytics, and intelligence -- over the internet to offer faster innovation and flexible resources.
- Leximancer
- A text mining software that can be used to analyze the content of collections of textual documents and to visually display the extracted information in a browser.
- Mendeley
- A reference manager and academic social network that helps users organize research, collaborate with others, and discover new research.
- OpenAI Pro for Teams
- OpenAI provides a simple interface to state-of-the-art AI models for natural language processing, image generation, semantic search, and speech recognition.
- Power Automate Desktop
- An application that allows users to automate repetitive tasks using robotic process automation (RPA).
- Python
- A computer programming language often used to build websites and software, automate tasks, and analyze data.
Data Sets
Our collaborators can use any data set we build. Examples of data sets that we've built so far include:
- 2023-24 job data for Evansville, Indianapolis, Louisville, Nashville, St. Louis and others.
- Online Forum and Social Post discussion of Blockchain, Generative AI, XR/VR/AR, Virtual Humans and more.
For additional data sets available to download or purchase, contact TAPLab coordinator Curt Gilstrap.