A business based on trust: the contribution of text mining as a big data technique to identify consumer insights to support the business of airbnb / Uma empresa baseada na confiança: a contribuição da mineração de texto como uma grande técnica de dados para identificar as percepções dos consumidores para apoiar o negócio da airbnb


  • Cláudia Pereira Ignácio Zuppo
  • Gustavo Corrêa Mirapalheta
  • João Luiz Chela




Airbnb, Text Mining, Sentiment Analysis, Bigrams, Tf-Idf.


This study uses a quantitative technique (Text Mining) coupled with a qualitative (subjective) approach to identify Airbnb’s key supporting business factors. This coupled approach demonstrates the feasibility of using a Big Data technique side-by-side with a “humanized” approach, Letouzé (2014), when the author says that Big Data exists “by the people and for the people”. To do so, it is used a database of nearly one million guests reviews of New York City properties made from 2008 through 2018. The adopted analytical approaches in Text Mining used the knowledge explored by Silge and Robinson, in their book “Text Mining with R” (2017), which includes the examination of Word Frequency, Bigrams, Sentiment Analysis using NRC Lexicon, Term Frequency Times Inverse Document Frequency (tf_idf), Zipf´s Law and Bigrams Network. All these analyses where developed from an historical perspective, since the Airbnb’s reviews database divides its data in 5 points in time: 2009-2010, 2011-2012, 2013-2014, 2015-2016 and 2017-2018 (this last year up to June). Main results confirmed that Trust is a key issue for the development and growth of Airbnb business. Word Frequency and Bigrams prove that Airbnb success goes way beyond comfortable and well located, properties, but instead they are mainly a first-step towards the development of human relations based on Trust. Learnings provide actionable insights both for Airbnb and for the segment of travel as a whole in their quest of conquering new customers in the future. 


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