Abstract This chapter is concerned with textual and sentiment analysis in agriculture commodities market using the natural language processing (NLP) methods. There are extensive research on textual and sentiment analysis in financial markets however, most of them are focusing on equity market and a minority on other commodities like energy commodities. Therefore, this chapter first reviews research works on textual and sentiment analysis in agriculture market in general. Then, presents textual analysis methods that can be carried out to study the effect of textual data and sentiment in agriculture market. Finally, it presents an example of implementing a topic modelling task and textual regression for forecasting realized volatility of corn returns. To the best of the author’s knowledge, there is no study focusing on textual regression in agriculture market. Additionally, the studies conducting textual sentiment analysis are very limited. In this spirit, this study tries to fill this gap by introducing both well established and new textual and sentiment analysis methods to the agricultural researchers community. The limited experiment carried out with these methods in the present research testifies the superiority of the text-based models in explaining future movements of corn’s volatility. More specifically, the results of one-month-ahead realized volatility regression indicates statistically significant superior performance of both direct textual regression and sentiment regression compared to traditional methods like HAR and ARIMA. In addition, as the most accurate method, textual regression’s accuracy stands higher above that of the sentiment regression model.