indian airport reviews
TRANSCRIPT
Service Quality Analysis of Indian Metro Airports using Passenger Review Mining
2nd IEEE Conference - IHCI
Saveetha University
Chennai
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Hari Bhaskar S & Viral Rathod, Amadeus Labs
10-11th Mar 2016
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Agenda
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_Background
_Problem Statement
_Related Work
_Approach
_Experiment
_Results
_Limitations
_Future Work
_References
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_ Indian airports passenger traffic growing annually at the pace of 18.3% in 2015.
_ International Air Transport Association (IATA) predicts India to be the third largest air travel market by 2026.
_ Low-Cost Carriers (LCC) offering affordable prices, increasing disposable income, and growing air travel propensity to save time among Indian passengers.
_ Passengers expect best-in-class service and hassle-free travel experience for air travel.
_ The growing list of expectations includes minimal wait times, high levels of comfort like stress-free travel, shopping, entertainment, meals, cleanliness, and courtesy during interactions ©
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Background
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Problem Statement
• Objective is to evaluate customer satisfaction levels of Indian Airports
• Online Reviews from Skytrax – a popular rating agency• Bangalore, Kolkata, Chennai, Mumbai and New Delhi
Airports
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Skytrax Data Set
Airport # Reviews
Bangalore 67
Chennai 62
Kolkata 64
Mumbai 163
New Delhi 146
AirportNumber of Passengers (in millions)
Bangalore 9.061
Chennai 7.614
Kolkata 5.937
Mumbai 20.018
New Delhi 22.763
Country # Reviews
Australia 32
Canada 26
India 97
United Kingdom 96
United States 93
Traveler Location
# Reviews
India 97
International 353
Not Provided 71
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_ Developing service quality models UAE airports, Australian Airports
_ Fodness & Murray model is used in assessing service quality for South Africa, Bangkok and Swedish airports.
_ A research study on using twitter feedback to assess customer satisfaction of airline industry using sentiment analysis technique is done
_ Skytrax data for airlines(Lufthansa, United…) and processing reviews using sentiment analysis techniques
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Related Work
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_Choose negatively rated comments (< 6)
_No sentiment analysis needed
_Process text using NLP (Stanford library)
_Part of Speech Tagger
_Create a corpus of nouns and map them using a program
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Approach
Categories Mapped Terms
Staff
Rude, Rudeness, Slow, Slowness, Impolite, Callous, Staff,Polite
ProductivityConference, Business, Meetings, Center, Quite
DecorAesthetic, Art, Music,Culture, Prayer, Lounge
Maintenance
Duty-Free, Duty,Shops, Shopping, Restaurant, Food,Beverage, Cuisine, Variety, Toilet, Toilets, Smell, Clean
EfficiencyWaiting, Speed, Time,Checkin, Slow, Inefficient
Effectiveness
Facility, Facilities, Layout, Access, Accessibility, Baggage, Connecting,Transportation
Immigration ImmigrationSecurity Security
Airport Service
Quality
Interaction
Function Diversion
Efficiency Effectiveness
Security
ChecksStaff Immigration
Maintenance Decor Productivity
Fodness & Murray Model
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Use association rules or apriori to find frequent complaint items/words
Association helps to identify what traveler commonly need in an airport
Retail/Super market use them for “basket analysis
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Apriori Algorithm
Airport Service
Quality
Interaction
Function Diversion
Efficiency Effectiveness
Security
ChecksStaff Immigration
Maintenance Decor Productivity
9
27
123 23
14 16
26
Results – Bangalore Airport
Associated Items Instances/Total
Effectiveness, Maintenance 10/46
Effectiveness, Security, Maintenance
5/46
Airport Service
Quality
Interaction
Function Diversion
Efficiency Effectiveness
Security
ChecksStaff Immigration
Maintenance Decor Productivity
3
27
112 36
20 26
29
Results – Chennai Airport
Associated ItemsInstances/Total
Effectiveness, Maintenance22/56
Effectiveness, Security, Maintenance
8/46
Airport Service
Quality
Interaction
Function Diversion
Efficiency Effectiveness
Security
ChecksStaff Immigration
Maintenance Decor Productivity
2
21
9 25
18 19
22 2
Results – Kolkata Airport
Associated ItemsInstances/Total
Effectiveness, Maintenance13/40
Immigration, Maintenance, Effectiveness
6/40
Airport Service
Quality
Interaction
Function Diversion
Efficiency Effectiveness
Security
ChecksStaff Immigration
Maintenance Decor Productivity
12
68
230 43
52 46
40
Results – Mumbai Airport
Associated ItemsInstances/Total
Effectiveness, Maintenance 22/106
Effectiveness, Immigration, Maintenance
11/106
Airport Service
Quality
Interaction
Function Diversion
Efficiency Effectiveness
Security
ChecksStaff Immigration
Maintenance Decor Productivity
4
51
129 27
29 24
36
Results – New Delhi Airport
Associated ItemsInstances/Total
Effectiveness, Maintenance14/72
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_ Accuracy of NLP
_ Negative rated reviews might have positive comments
_ Corpus of nouns can be improved further
Limitations
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Twitter handles and Facebook pages for passenger reviews.
Benchmarking across Airports
Scope of refining service quality models and developing new ones based on review mining terms.
Future Work
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_ Delhi shows promise on the joint venture
_ Bangalore, Mumbai has concerns
_ Airports run by AAI like Kolkata and Chennai still going through modernization phase
_ Interaction aspects like the staff, immigration and security checks comes as a recurring theme of concern across all airports.
_ Mitigated through training and awareness initiatives
_ Interestingly queue wait time and delays are ranked relatively less.
_ Good airport facility, maintenance are basic necessities that passenger expects which takes precedence over the rest of categories.
_ This research study may be useful to devise a further course of actions
Conclusion
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_ India dominates air traffic growth: IATA, Business Standard, 3rd Oct 2015, http://www.business-standard.com/article/companies/india-dominates-air-traffic-growth-iata-115100200671_1.html
_ IATA Air Passenger Forecast Shows Dip in Long-Term Demand, IATA, 26th Nov 2015, https://www.iata.org/pressroom/pr/Pages/2015-11-26-01.aspx
_ Case Analysis: Manage Expectations, Business World, 17th Nov 2015, http://businessworld.in/article/Case-Analysis-Manage-Expectations/17-11-2015-87260/
_ Charges for Airports and Air navigation, AAI, http://www.aai.aero/misc/AirportCharges2014-15-221114.pdf
_ Annexure III- A , AAI, http://www.aai.aero/traffic_news/Sep2k15annex3.pdf
_ Kramer, Lois S., Aaron Bothner, and Max Spiro. How Airports Measure Customer Service Performance. Vol. 48. Transportation Research Board, 2013.
_ Fodness, D., Murray, B., 2007. Passengers’ expectations of airport service quality. Journal of Services Marketing 21, 492e506
_ Gupta, A., Arif, M., & Williams, A. (2013). Customer Service in Aviation Industry – An Exploratory Analysis of UAE Airports. Journal of Air Transport Management, 32(September 2013)
_ Park, Jin-Woo, Rodger Robertson, and Cheng-Lung Wu. "The effects of individual dimensions of airline service quality: Findings from Australian domestic air passengers." Journal of hospitality and tourism management13.02 (2006): 161-176.
_ Clemes, Michael D., et al. "An empirical analysis of customer satisfaction in international air travel." Innovative Marketing 4.2 (2008): 50-62.
_ Lubbe, B., et al., “An application of the airport service quality model in South Africa”, Journal of Air TransportManagement (2010), doi:10.1016/j.jairtraman.2010.08.001
_ Farmahini Farahani, Aliakbar, and Emil Törmä. "Assessment of customers' service quality expectations: Testing the'Hierarchical Structure for Airport Service Quality Expectations' in a Swedish context." (2010).
_ Seyanont, Arisara. "Passengers’ Perspective toward Airport Service Quality at Suvarnabhumi International Airport." (2011).
_ Misopoulos, Fotis, et al. "Uncovering customer service experiences with Twitter: the case of the airline industry." Management Decision 52.4 (2014): 705-723.
_ Suzuki, Takayuki, Kiminori Gemba, and Atsushi Aoyama. "Identifying customer satisfaction estimators using review mining." International Journal of Technology Marketing 5 9.2 (2014): 187-210.
_ Archak, Nikolay, Anindya Ghose, and Panagiotis G. Ipeirotis. "Show me the money!: deriving the pricing power of product features by mining consumer reviews." Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007.
_ Hu, Minqing, and Bing Liu. "Mining and summarizing customer reviews."Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004.
_ Liu, Bing, Minqing Hu, and Junsheng Cheng. "Opinion observer: analyzing and comparing opinions on the web." Proceedings of the 14th international conference on World Wide Web. ACM, 2005.
_ Zhang, Haiping, et al. "Feature-level sentiment analysis for Chinese product reviews." Computer Research and Development (ICCRD), 2011 3rd International Conference on. Vol. 2. IEEE, 2011.
_ Polpinij, Jantima, and Aditya K. Ghose. "An ontology-based sentiment classification methodology for online consumer reviews." Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, 2008.
References