Sustainability has become increasingly important to corporations, as stakeholders have called for increased transparency and as corporations have recognized the benefits of considering corporate sustainability. As a result, there has been a dramatic increase in disclosure both through corporate statements and through annual reports in which companies will describe the environmental activities in which they are involved. These documents and reports are of interest to researchers because they represent a wealth of information that can be studied and analyzed. In the past, the contents of these reports have been studied through manual methods; however, there is a great potential for automatic analysis of these reports. This paper will document the methodology taken to produce an automated analytics software that produces outputs that can further be used in analysis. Specifically, the program is meant to calculate the word frequencies of certain words and phrases that are of interest and it also extracts the sentences in which these words or phrases are contained. In this research, the output of the program is used in 2 applications. One regresses the sustainability word frequencies against a published sustainability score and another application uses a simple form of sentiment analysis to analyze the positive and negative sentiment of the extracted sentences. Human methods are usually used to perform tasks such as sentiment analysis and frequency count. The program created in this research provides a first step toward future computational analysis work. While the program is able to perform the tasks for which it was designed, improvements can be made to produce a more comprehensive and versatile program.