The explosion of digitized textual big data (TBD) has provided opportunities for academic consumer research in the hospitality and tourism industry. However, its unstructured nature and large size pose challenges to researchers. This study proposes a standardized TBD research framework and introduces practical methods used in R and Python, including the advantages and disadvantages of various methodological approaches. Furthermore, the study provides a comprehensive review of hospitality and tourism literature on TBD, focused on sentiment analysis and topic modeling. Results show that TBD analytics in hospitality and tourism is still in its infancy, with little attention paid to methodological rigorousness and a lack of detailed descriptions. It contributes to the literature on TBD by providing research suggestions for state-of-the-art methods and skills based on the proposed framework and the literature review.