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

• Agarwal B, Mittal N (2014) Prominent feature extraction for review analysis: an empirical study. J Exp Theor Artif Intell 28(3):485–498

  Article  Google Scholar 

• Agarwal B, Mittal N, Bansal P, Garg S (2015) Sentiment analysis using common-sense and context information. Comput Intell Neurosci 2015:1–9

  Article  Google Scholar 

• Airbnb (2014) Building trust with a new review system. http://blog.airbnb.com/building-trust-new-review-system/. Accessed 11 May 2017

• 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)

• Belk R (2007) Why not share rather than own? Ann Am Acad Polit Soc Sci 611(1):126–140

  Article  Google Scholar 

• Belk R (2010) Sharing. J Consum Res 36(5):715–734

  Article  Google Scholar 

• Belk R (2014) You are what you can access: sharing and collaborative consumption online. J Bus Res 67(8):1595–1600

  Article  Google Scholar 

• 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 339–348

• Botsman R (2013) The sharing economy lacks a shared definition. http://www.fastcoexist.com/3022028/the-sharing-economy-lacks-a-shared-definition. Accessed 15 May 2017

• Botsman R, Rogers R (2010) Beyond zipcar: collaborative consumption. Harvard business review, Cambridge

  Google Scholar 

• 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 581–586

• Google Inc (2016) Announcing syntaxNet: the world’s most accurate parser goes open source. https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html. Accessed 14 May 2017

• 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)

• Hamari J, Sjöklint M, Ukkonen A (2015) The sharing economy: why people participate in collaborative consumption. J Assoc Inf Sci Technol 67(9):2047–2059

  Article  Google Scholar 

• Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. Manag Inf Syst Q 28(1):75–105

  Article  Google Scholar 

• 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 168–177

• IBM (2015) Sentiment analysis with AlchemyAPI: a hybrid approach. Tech. rep, IBM Cooperation, Somers, NY

• 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 56–65

• Liu B (2012) Sentiment analysis and opinion mining. Morgan & Claypool

• Liu B (2015) Sentiment analysis—mining opinions, sentiments, and emotions, 1st edn. Cambridge University Press, New York

  Book  Google Scholar 

• 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 342–351

• 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 55–60

• March ST, Smith GF (1995) Design and natural science research on information technology. Decis Support Syst 15(4):251–266

  Article  Google Scholar 

• 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):336–354

  Article  Google Scholar 

• Marwick AE, Boyd D (2011) I tweet honestly, i tweet passionately: twitter users, context collapse, and the imagined audience. New Media Soc 13(1):114–133

  Article  Google Scholar 

• 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 19

• Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41

  Article  Google Scholar 

• 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 70–77

• 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 1659–1666

• Owyang J (2015) Large companies ramp up adoption in the collaborative economy. http://www.web-strategist.com/blog/2015/07/20/large-companies-ramp-up-adoption-in-the-collaborative-economy/. Accessed 18 May 2017

• Owyang J, Tran C, Silva C (2013) The collaborative economy. Tech. rep, Altimeter Group, San Maeto, CA

• Page R (2012) The Linguistics of Self-Branding and Micro-Celebrity in Twitter: The Role of Hashtags. Discourse Commun 6(2):181–201

  Article  Google Scholar 

• Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135

  Article  Google Scholar 

• 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, Portugal

• 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 247–250

• 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 50–64

  Chapter  Google Scholar 

• 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 44–53

• 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 508–524

• Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manag 52(1):5–19

  Article  Google Scholar 

• 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 2371–2378

• 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):1–11

  Google Scholar 

• 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 712–717

• 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 119–134

• Walsh B (2011) 10 ideas that will change the world. https://content.time.com/time/specials/packages/article/0,28804,2059521_2059564,00.html. Accessed 14 May 2017

• 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 347–354

• 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 55–56

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