On the first day, the workshop will cover the necessary steps involved in text analysis and discuss the application of various statistical software tools. On the second and third days of the workshop, we explore the applications of machine learning (ML) in text analysis. Day two will cover some fundamental algorithms such as OLS, logistic regression, and support vector machines, as well as K-Nearest Neighbors (KNN). Participants will learn how to leverage these methods to derive insights from text data. The final day will explore advanced topic modeling techniques, including Latent Dirichlet Allocation (LDA), Structural Topic Models (STM), keyATM as well as word embeddings. This workshop offers valuable skills primarily for academic research as well as techniques that could be used in private sector. Participants are very welcome and encouraged to bring their project ideas or ongoing projects that involve unstructured text data and text methods. In addition to the workshop, we will offer office hours where attendees can have one-on-one discussions with the instructor to get personalized guidance on their projects, corpus creation, text vectorization, and the use of text methods. We will primarily use R for this workshop.