This research full paper proposes opportunities to expand qualitative and textual data analysis using natural language processing (NLP), and demonstrates opportunities to use NLP in engineering education work in our presentation of two NLP-based projects as examples of how NLP can be used. The discipline of engineering education frequently employs qualitative data analysis techniques, but fundamentally, the analysis of large corpora of textual documents is limited by researcher's time. In this paper, we present a brief review literature of how NLP has been used in other qualitative research fields. This portion of the paper is aimed to provide a clear description of NLP for those unfamiliar with machine learning and natural language processing methods. The second part of the paper will provide two brief examples of how NLP is being employed in our research group. Example 1 is a study of engineering résumés, with the intention of being able to calculate the 'disciplinary discourse density' based off the engineering language presented in engineering résumés, a technique validated in prior qualitative studies by hand. Example 2 is a genre analysis of engineering literature reviews, seeking to understand the ways in which sentences linguistically build into arguments, such that the task of writing literature reviews might be demystified. This paper will have a methodological impact for researchers attempting to use NLP methods to analyze qualitative data, articulating the opportunities and barriers in using these methods for engineering education research.