Topic Modeling and Sentiment Analysis of Online Review for Airlines
Abstract
:1. Introduction
2. Literature Review
2.1. Asian Airline
2.2. Online Review
2.3. Topic Modeling and Sentiment Analysis Using Big Data
- for word in the document:
- Choose a topic zi,j~Multinomial(p(z|di))
- Choose a topic wi,j~Multinomial(p(w|zi,j))
3. Materials and Methods
4. Results
4.1. Frequency Analysis
4.2. Word Cloud
4.3. Topic Modeling
4.4. Sentiment Analysis
5. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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---|---|---|---|
Lucini, F.R.; Tonetto, L.M.; Fogliatto, F.S.; Anzanello, M.J. [36] | 2020 | Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews | 55,000 reviews covering 400 airlines and passengers from 170 countries analyzed using latent Dirichlet allocation (LDA) model, and identified 27 dimensions of satisfaction. |
Kim, S.; Park, H.; Lee, J. [37] | 2020 | Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis | This paper applies LDA topic modeling to a vast number of passenger’s online review to compare service quality between full service carriers and low cost carriers. |
Sutherland, I.; Sim, Y.; Lee, S.K.; Byun, J.; Kiatkawsin, K. [38] | 2020 | Topic Modeling of Online Accommodation Reviews via Latent Dirichlet Allocation | This paper applied an inductive approach by utilizing large unstructured text data of 104,161 online reviews of Korean accommodation customers to frame which topics of interest guests find important. |
Lim, J.; Lee, H.C. [39] | 2019 | Comparisons of service quality perceptions between full service carriers and low cost carriers in airline travel | This paper proposed a new topic modeling method called Word2vec-based Latent Semantic Analysis to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. |
Sun, L.; Yin, Y. [40] | 2017 | Discovering themes and trends in transportation research using topic modeling | This paper applied a LDA model on article abstracts to infer 50 key topics. We show that those characterized topics are both representative and meaningful, mostly corresponding to established subfields in transportation research. |
Rank | Word | Freq. | Rank | Word | Freq. | Rank | Word | Freq. |
---|---|---|---|---|---|---|---|---|
1 | flight | 20,863 | 35 | inflight | 1877 | 69 | sydney | 1013 |
2 | seat | 9798 | 36 | back | 1856 | 70 | southern | 989 |
3 | service | 7395 | 37 | hongkong | 1783 | 71 | didn’t | 983 |
4 | airline | 6945 | 38 | nice | 1771 | 72 | luggage | 982 |
5 | food | 6913 | 39 | lounge | 1745 | 73 | movies | 978 |
6 | good | 6744 | 40 | leg | 1739 | 74 | board | 974 |
7 | time | 5431 | 41 | fly | 1730 | 75 | told | 967 |
8 | crew | 4640 | 42 | served | 1638 | 76 | bit | 963 |
9 | class | 4583 | 43 | trip | 1609 | 77 | room | 957 |
10 | cabin | 4547 | 44 | checkin | 1549 | 78 | choice | 952 |
11 | staff | 4529 | 45 | delayed | 1547 | 79 | premium | 948 |
12 | hour | 4275 | 46 | beijing | 1506 | 80 | manila | 942 |
13 | meal | 3841 | 47 | clean | 1502 | 81 | onboard | 927 |
14 | business | 3360 | 48 | attendant | 1490 | 82 | quality | 924 |
15 | economy | 3065 | 49 | long | 1469 | 83 | provided | 922 |
16 | comfortable | 2975 | 50 | drinks | 1428 | 84 | small | 919 |
17 | entertainment | 2943 | 51 | london | 1393 | 85 | selection | 905 |
18 | return | 2818 | 52 | flying | 1360 | 86 | english | 989 |
19 | great | 2579 | 53 | ground | 1338 | 87 | departure | 895 |
20 | singapore | 2535 | 54 | life | 1228 | 88 | cathay | 894 |
21 | flew | 2493 | 55 | arrived | 1222 | 89 | asked | 892 |
22 | plane | 2475 | 56 | due | 1198 | 90 | price | 887 |
23 | friendly | 2414 | 57 | full | 1182 | 91 | ticket | 875 |
24 | excellent | 2384 | 58 | helpful | 1175 | 92 | pleasant | 862 |
25 | china | 2341 | 59 | minutes | 1168 | 93 | poor | 861 |
26 | airport | 2339 | 60 | airways | 1140 | 94 | booked | 860 |
27 | aircraft | 2227 | 61 | made | 1112 | 95 | thai | 856 |
28 | air | 2174 | 62 | offered | 1109 | 96 | boeing | 848 |
29 | boarding | 2128 | 63 | attentive | 1097 | 97 | times | 847 |
30 | bangkok | 2081 | 64 | efficient | 1087 | 98 | bad | 842 |
31 | check | 2066 | 65 | late | 1071 | 99 | gate | 834 |
32 | experience | 2016 | 66 | short | 1035 | 100 | left | 808 |
33 | passenger | 1936 | 67 | shanghai | 1022 | |||
34 | guangzhou | 1886 | 68 | system | 1014 |
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Kwon, H.-J.; Ban, H.-J.; Jun, J.-K.; Kim, H.-S. Topic Modeling and Sentiment Analysis of Online Review for Airlines. Information 2021, 12, 78. https://doi.org/10.3390/info12020078
Kwon H-J, Ban H-J, Jun J-K, Kim H-S. Topic Modeling and Sentiment Analysis of Online Review for Airlines. Information. 2021; 12(2):78. https://doi.org/10.3390/info12020078
Chicago/Turabian StyleKwon, Hye-Jin, Hyun-Jeong Ban, Jae-Kyoon Jun, and Hak-Seon Kim. 2021. "Topic Modeling and Sentiment Analysis of Online Review for Airlines" Information 12, no. 2: 78. https://doi.org/10.3390/info12020078
APA StyleKwon, H. -J., Ban, H. -J., Jun, J. -K., & Kim, H. -S. (2021). Topic Modeling and Sentiment Analysis of Online Review for Airlines. Information, 12(2), 78. https://doi.org/10.3390/info12020078