The travel accommodation industry has changed drastically due to the
sharing economy. This sharing economy depends significantly on crowdsourcing in the form of quantitative and
qualitative reviews. In this thesis, we study the impact of expressed sentiments in the text reviews on listing prices;
we explore differences in the factors that lead to satisfaction according to customer origin and the effects of external
shocks on Airbnb´s prices. We employ NLP to extract the sentiment and emotion scores to be modeled as a
function of different characteristics and geophysical information. Our study provides an important contribute to the
research on pricing and customer satisfaction in the sharing economy by finding a significant effect of the
expression of positive sentiments on prices, describing the differences in the factors that lead to satisfaction
according to customer origin, and revealing that guests moved away from main municipalities and valued listings
with more bedrooms during the Covid-19 pandemic
