MengFDipietroRBGerdesJ, et al.
How hotel responses to negative online reviews affect customers’ perception of hotel image and behavioral intent: an exploratory investigation. Tour Rev Int2018;
22: 23–39.
2.
LeeCCHuC.Analyzing hotel customers’ E-complaints from an internet complaint forum. J Travel Tour Market2005;
17: 167–181.
3.
TaxSSBrownSWChandrashekaranM.Customer evaluations of service complaint experiences: implications for relationship marketing. J Market1998; 62: 60–76.
4.
CvelbarLKMayrMVavpoticD.Geographical mapping of visitor flow in tourism: a user-generated content approach. Tour Econ2018;
24: 701–719.
5.
GuBYeQ.First step in social media: measuring the influence of online management responses on customer satisfaction. Prod Oper Manage2014;
23: 570–582.
6.
GuoYSunSSchuckertM, et al.
Online feedback and attraction management: an exploration of the critical factors in effective operations. Asia Pacific J Tour Res2016;
21: 883–904.
7.
FangY-HTangKLiC-Y, et al.
On electronic word-of-mouth diffusion in social networks: curiosity and influence. Int J Advert2018;
37: 360–384.
8.
SparksBABrowningV.Complaining in cyberspace: the motives and forms of hotel guests’ complaints online. J Hospital Market Manage2010;
19: 797–818.
PitsilisGKRamampiaroHLangsethH.Effective hate-speech detection in Twitter data using recurrent neural networks. Appl Intell2018; 48: 1–13.
11.
GuerreiroJMoroS.Are Yelp’s tips helpful in building influential consumers?Tour Manage Perspect2017;
24: 151–154.
12.
KimIChoM.The impact of brand relationship and attributions on passenger response to service failure. Asia Pacific J Tour Res2014;
19: 1441–1462.
13.
AssafAGCvelbarLK.Why negative outputs are often ignored: a comprehensive measure of hotel performance. Tourism Econ2015;
21: 761–773.
14.
ChengVTLoiMK.Handling negative online customer reviews: the effects of elaboration likelihood model and distributive justice. J Travel Tour Market2014;
31: 1–15.
15.
XieKLZhangZZhangZ.The business value of online consumer reviews and management response to hotel performance. Int J Hospital Manage2014;
43: 1–12.
16.
AdamopoulosPGhoseATodriV.The impact of user personality traits on word of mouth: text-mining social media platforms. Inform Syst Res2018;
29: 612–640.
17.
MoroSRitaPCoelhoJ.Stripping customers’ feedback on hotels through data mining: the case of Las Vegas Strip. Tour Manage Perspect2017;
23: 41–52.
18.
BhardwajPKhoslaP.Review of text mining techniques. IITM J Manage IT2017;
8: 27–31.
19.
LiuXWangWHeD, et al.
Semi-supervised community detection based on non-negative matrix factorization with node popularity. Inform Sci2017;
381: 304–321.
20.
HanDGiraud-CarrierCLiS.Efficient mining of high-speed uncertain data streams. Appl Intell2015;
43: 773–785.
21.
GrönroosC.A service quality model and its marketing implications. Eur J Market2013;
18: 36–44.
22.
BitnerMJBrownSWMeuterML.Technology infusion in service encounters. J Acad Market Sci2000;
28: 138
23.
ShinHEllingerAEMothersbaughDL, et al.
Employing proactive interaction for service failure prevention to improve customer service experiences. J Serv Theory Pract2017;
27: 164–186.
24.
HogreveJBilsteinNBilsteinN.Service recovery on stage: effects of social media recovery on virtually present others. J Serv Res2019;
22.
25.
JamesTLCalderonEDVCookDF.Exploring patient perceptions of healthcare service quality through analysis of unstructured feedback. Expert Syst Appl2016;
71: 479–492.
26.
Denizci GuilletBKucukustaDLiuL.An examination of social media marketing in China: how do the top 133 hotel brands perform on the top four Chinese social media sites. J Travel Tour Market2016;
33: 783–805.
27.
SchuckertMLiuXLawR.Hospitality and tourism online reviews: recent trends and future directions. J Travel Tour Market2015;
32: 608–621.
28.
KimAJJohnsonKK.Power of consumers using social media. Comput Hum Behav2016;
58: 98–108.
29.
MalhotraSDixitA.An effective approach for news article summarization. Int J Comput Appl2014;
76: 5–10.
30.
AlmeidaTASilvaTPSantosI.Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering. Knowl-Based Syst2016;
108: 25–32.
31.
LiNWuDD.Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis Support Syst2010;
48: 354–368.
32.
AbrahamsASJiaoJWangGA, et al.
Vehicle defect discovery from social media. Decis Support Syst2012;
54: 87–97.
33.
TussyadiahIPZachF.Identifying salient attributes of peer-to-peer accommodation experience. J Travel Tour Market2017;
34: 636–652.
34.
LiNTungVLawR.A fuzzy comprehensive evaluation algorithm for analyzing electronic word-of-mouth. Asia Pacific J Tour Res2017;
22: 592–603.
35.
FungGPCYuJXLuH, et al.
Text classification without negative examples revisit. IEEE Trans Knowl Data Eng2005;
18: 6–20.
36.
ZhouDSchölkopfB. Learning from labeled and unlabeled data using random walks. In: International joint conference on neural networks,Anchorage, USA, 14-19 May 2017.
37.
AdeliEThungK-HAnL, et al.
Semi-supervised discriminative classification robust to sample-outliers and feature-noises. IEEE Trans Pattern Anal Mach Intell2019;
41: 515–522.
38.
IencoDPensaRG.Positive and unlabeled learning in categorical data. Neurocomputing2016;
196: 113–124.
39.
NotoKSaierMHElkanC. Learning to find relevant biological articles without negative training examples. In: Proceedings of AI 2008: advances in artificial intelligence, Australasian joint conference on artificial intelligence, Auckland, New Zealand, 1–5 December 2008.
40.
ZhangT. The value of unlabeled data for classification problems. In: The seventeenth international conference on machine learning, Stanford University, Stanford, CA, USA, 29 June - 2 July, 2000.
41.
RocchioJ.Relevance feedback in information retrieval. Comput Sci2000; 41: 313–323.
42.
SaltonGBuckleyC.Term-weighting approaches in automatic text retrieval. Inform Process Manage1988;
24: 513–523.
43.
LiuQZhangHPYuHK, et al.
Chinese lexical analysis using cascaded hidden Markov model. J Comput Res Dev2004;
41: 1421–1429.
44.
SarwarBKarypisGKonstanJ, et al. Item-based collaborative filtering recommendation algorithms. In: The international conference on world wide web, Hong Kong, 1-5 May, 2000.
45.
PengHLongFDingC.Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell2005;
27: 1226–1238.
46.
CombarroEFMontanesEDiazI, et al.
Introducing a family of linear measures for feature selection in text categorization. IEEE Trans Knowl Data Eng2005;
17: 1223–1232.
47.
QuinlanJR.Induction of decision trees. Mach Learn1986;
1: 81–106.
CoverTHartP.Nearest neighbor pattern classification. IEEE Trans Inform Theory1967;
13: 21–27.
50.
JohnGHLangleyP.Estimating continuous distributions in Bayesian classifiers. In:Proceedings of the eleventh conference on uncertainty in artificial intelligence, Montreal, QU, 18 - 20 August 1995.
51.
DumaisSPlattJHeckermanD, et al.
Inductive learning algorithms and representations for text categorization. In: Seventh International Conference on Information and Knowledge Managemen, Bethesda, Maryland, USA, 02-07 November 1998.
52.
HuangL.Social media as a new play in a marketing channel strategy: evidence from Taiwan travel agencies’ blogs. Asia Pacific J Tour Res2012;
17: 615–634.