Leveraging social media to gain insights into service delivery: a study on Airbnb

  • Moritz von Hoffen
  • Marvin Hagge
  • Jan Hendrik BetzingEmail author
  • Friedrich Chasin
Original Article


Consumers increasingly rely on reviews and social media posts provided by others to get information about a service. Especially in the Sharing Economy, the quality of service delivery varies widely; no common quality standard can be expected. Because of the rapidly increasing number of reviews and tweets regarding a particular service, the available information becomes unmanageable for a single individual. However, this data contains valuable insights for platform operators to improve the service and educate individual providers. Therefore, an automated tool to summarize this flood of information is needed. Various approaches to aggregating and analyzing unstructured texts like reviews and tweets have already been proposed. In this research, we present a software toolkit that supports the sentiment analysis workflow informed by the current state-of-the-art. Our holistic toolkit embraces the entire process, from data collection and filtering to automated analysis to an interactive visualization of the results to guide researchers and practitioners in interpreting the results. We give an example of how the tool works by identifying positive and negative sentiments from reviews and tweets regarding Airbnb and delivering insights into the features of service delivery its users most value and most dislike. In doing so, we lay the foundation for learning why people participate in the Sharing Economy and for showing how to use the data. Beyond its application on the Sharing Economy, the proposed toolkit is a step toward providing the research community with an instrument for a holistic sentiment analysis of individual domains of interest.


Social media analysis Sentiment analysis Sharing Economy Service delivery 


  1. Agarwal B, Mittal N (2014) Prominent feature extraction for review analysis: an empirical study. J Exp Theor Artif Intell 28(3):485–498CrossRefGoogle Scholar
  2. Agarwal B, Mittal N, Bansal P, Garg S (2015) Sentiment analysis using common-sense and context information. Comput Intell Neurosci 2015:1–9CrossRefGoogle Scholar
  3. Airbnb (2014) Building trust with a new review system. Accessed 11 May 2017
  4. Andersson M, Hjalmarsson A, Avital M (2013) Peer-to-peer service sharing platforms: driving share and share alike on a mass-scale. In: Proceedings of the international conference on information systems (ICIS ’13)Google Scholar
  5. Belk R (2007) Why not share rather than own? Ann Am Acad Polit Soc Sci 611(1):126–140CrossRefGoogle Scholar
  6. Belk R (2010) Sharing. J Consum Res 36(5):715–734CrossRefGoogle Scholar
  7. Belk R (2014) You are what you can access: sharing and collaborative consumption online. J Bus Res 67(8):1595–1600CrossRefGoogle Scholar
  8. Blair-Goldensohn S, Hannan K, McDonald R, Neylon T, Reis G, Reynar J (2008) Building a sentiment summarizer for local service reviews. In: WWW workshop on NLP in the information explosion era. Beijing, China, pp 339–348Google Scholar
  9. Botsman R (2013) The sharing economy lacks a shared definition. Accessed 15 May 2017
  10. Botsman R, Rogers R (2010) Beyond zipcar: collaborative consumption. Harvard business review, CambridgeGoogle Scholar
  11. González-Ibáñez R, Muresan S, Wacholder N (2011) Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 581–586Google Scholar
  12. Google Inc (2016) Announcing syntaxNet: the world’s most accurate parser goes open source. Accessed 14 May 2017
  13. Hagge M, von Hoffen M, Betzing JH, Becker J (2017) Design and implementation of a toolkit for the aspect-based sentiment analysis of tweets. In: Proceedings of the 19th IEEE conference on business informatics (CBI ’17)Google Scholar
  14. Hamari J, Sjöklint M, Ukkonen A (2015) The sharing economy: why people participate in collaborative consumption. J Assoc Inf Sci Technol 67(9):2047–2059CrossRefGoogle Scholar
  15. Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. Manag Inf Syst Q 28(1):75–105CrossRefGoogle Scholar
  16. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’04), pp 168–177Google Scholar
  17. IBM (2015) Sentiment analysis with AlchemyAPI: a hybrid approach. Tech. rep, IBM Cooperation, Somers, NYGoogle Scholar
  18. Java A, Song X, Finin T, Tseng B (2007) Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on web mining and social network analysis (WebKDD/SNA-KDD ’07), pp 56–65Google Scholar
  19. Liu B (2012) Sentiment analysis and opinion mining. Morgan & ClaypoolGoogle Scholar
  20. Liu B (2015) Sentiment analysis—mining opinions, sentiments, and emotions, 1st edn. Cambridge University Press, New YorkCrossRefGoogle Scholar
  21. Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on world wide web (WWW ’05). Chiba, Japan, pp 342–351Google Scholar
  22. Manning CD, Bauer J, Finkel J, Bethard SJ, Surdeanu M, McClosky D (2014) The stanford coreNLP natural language processing toolkit. In: Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. Baltimore, Maryland, pp 55–60Google Scholar
  23. March ST, Smith GF (1995) Design and natural science research on information technology. Decis Support Syst 15(4):251–266CrossRefGoogle Scholar
  24. Marchand A, Hennig-Thurau T, Wiertz C (2017) Not all digital word of mouth is created equal: understanding the respective impact of consumer reviews and microblogs on new product success. Int J Res Mark 34(2):336–354CrossRefGoogle Scholar
  25. Marwick AE, Boyd D (2011) I tweet honestly, i tweet passionately: twitter users, context collapse, and the imagined audience. New Media Soc 13(1):114–133CrossRefGoogle Scholar
  26. Matzner M, Chasin F, Todenhöfer L (2015) To share or not to share towards understanding the antecedents of participation in it-enabled sharing services. In: Proceedings of the 23th Eeuropean conference on information systems (ECIS ’15), p 19Google Scholar
  27. Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41CrossRefGoogle Scholar
  28. Nasukawa T, Yi J (2003) Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd international conference on knowledge capture (K-CAP ’03). ACM, New York, pp 70–77Google Scholar
  29. Nivre J, de Marneffe MC, Ginter F, Goldberg Y, Hajic J, Manning CD, McDonald R, Petrov S, Pyysalo S, Silveira N, Others (2016) Universal dependencies v1: a multilingual treebank collection. In: Proceedings of the 10th international conference on language resources and evaluation (LREC ’16). Portorož, Slovenia, pp 1659–1666Google Scholar
  30. Owyang J (2015) Large companies ramp up adoption in the collaborative economy. Accessed 18 May 2017
  31. Owyang J, Tran C, Silva C (2013) The collaborative economy. Tech. rep, Altimeter Group, San Maeto, CAGoogle Scholar
  32. Page R (2012) The Linguistics of Self-Branding and Micro-Celebrity in Twitter: The Role of Hashtags. Discourse Commun 6(2):181–201CrossRefGoogle Scholar
  33. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRefGoogle Scholar
  34. Plenter F, Fielt E, Chasin F, Rosemann M (2017) Repainting the business model canvas for peer-to-peer sharing and collaborative consumption. In: Proceedings of the 25th European conference on information systems (ECIS ’17), Guimaraes, PortugalGoogle Scholar
  35. Quercia D, Askham H, Crowcroft J (2012) TweetLDA: supervised topic classification and link prediction in twitter. In: Proceedings of the 4th annual ACM web science conference (WebSci ’12). ACM, New York, pp 247–250Google Scholar
  36. Rajman M, Besançon R (1998) Text mining: natural language techniques and text mining applications. In: Spaccapietra S, Maryanski F (eds) Data mining and reverse engineering, 1st edn. Springer, Leysin, pp 50–64CrossRefGoogle Scholar
  37. Rizzo G, Cano Basave AE, Pereira B, Varga A (2015) Making sense of microposts. In: Proceedings of the 5th workshop on making sense of microposts (#Microposts 2015) at the 24th international conference on the world wide web (WWW ’15). Florence, Italy, pp 44–53Google Scholar
  38. Saif H, He Y, Harith A (2012) Semantic sentiment analysis of twitter. In: Proceedings of the 11th international conference on the semantic web (ISWC ’12), Bosten, vol 7649, pp 508–524Google Scholar
  39. Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manag 52(1):5–19CrossRefGoogle Scholar
  40. Schuster S, Manning CD (2016) Enhanced english universal dependencies: an improved representation for natural language understanding tasks. In: Proceedings of the 10th international conference on language resources and evaluation (LREC ’16), pp 2371–2378Google Scholar
  41. Simon T, Goldberg A, Aharonson-Daniel L, Leykin D, Adini B (2014) Twitter in the cross fire—the use of social media in the Westgate Mall terror attack in Kenya. PLoS ONE 9(8):1–11Google Scholar
  42. Singh VK, Piryani R, Uddin A, Waila P (2013) Sentiment analysis of movie reviews: a new feature-based heuristic for aspect-level sentiment classification. In: 2013 international mutli-conference on automation, computing, communication, control and compressed sensing (iMac4s ’13), pp 712–717Google Scholar
  43. Thelwall M (2017) The heart and soul of the web? Sentiment strength detection in the social web with sentistrength. In: Cyberemotions: collective emotions in cyberspace, understanding complex systems. Springer International Publishing, pp 119–134Google Scholar
  44. Walsh B (2011) 10 ideas that will change the world.,28804,2059521_2059564,00.html. Accessed 14 May 2017
  45. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing (HLT ’05). Association for computational linguistics, Stroudsburg, PA, USA, pp 347–354Google Scholar
  46. Yamada I, Takeda H, Takefuji Y (2015) an end-to-end entity linking approach for tweets. In: 5th workshop on making sense of microposts (#Microposts 2015) at the 24th international conference on the world wide web (WWW ’15), Florence, Italy, vol 1395, pp 55–56Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Moritz von Hoffen
    • 1
  • Marvin Hagge
    • 1
  • Jan Hendrik Betzing
    • 1
    Email author
  • Friedrich Chasin
    • 1
  1. 1.European Research Center for Information Systems (ERCIS), University of MuensterMünsterGermany

Personalised recommendations