Farm tourism experiences in travel reviews: A cross-comparison of three alternative methods for data analysis

This paper demonstrates three alternative approaches for mining consumer sentiment from large amounts of qualitative data found in online travel reviews. Manual content coding, corpus-based semantic analysis, and stance-shift analysis represent methods varying greatly in both process and output. For illustration purposes, they are applied in an exploratory study focused on consumers' reaction to farm stays in order to demonstrate how large volumes of qualitative data can be analyzed quantitatively in a relatively efficient and reliable way. A total of 800 narratives describing farm stay experiences and representing four national settings (Australia, Italy, UK, and USA) was collected. The results reveal that each method provides unique insights of what a farm stay vacation evokes, helpful to farm entrepreneurs wishing to develop a tourism business.

The findings indicate universal values as recurrent key drivers of customer satisfaction closely relate to rural experiences. Comparing the national datasets, local differences are evident, highlighting regional variations in terms of service products and consumer preferences. From a methodological viewpoint, all three methods produce reliable results by evidence of the similarities across all three analyses. The current study shows manual content coding, corpus-based semantic method, and stance-shift analysis can capture the peculiarities of rural experiences in different national settings.