This document summarizes a study analyzing images contained in online hotel reviews. The study used a deep learning model to classify over 86,000 images from TripAdvisor reviews into 1,000 categories. It found that while images classified as "non-hotel" aspects made up over half the images, these types of images were more common in positive reviews. Images classified as showing potential problems like insects were more frequent in negative reviews. The study aims to better understand how image content relates to review scores and customer sentiment.

1. A Preliminary Analysis of Images
in Online Hotel Reviews
Florian J. Zacha
, Yufeng Mab
, and Edward A Foxb
a
Hospitality & Tourism Management, Virginia Tech, USA
b
Department of Computer Science, Virginia Tech, USA

2. Introduction
• User generated content is king
• Hotel reviews affects
• Attitude towards hotels (Vermeulen and Seegers, 2009)
• Booking intention (Tsao, Hsieh, Shih, and Lin, 2015)
• Pictures in hotel reviews are
• Useful indicators of customer experiences (Xiang, Du, Ma, and Fan, 2018)
• AND include pictures of non-hotel aspects of a stay
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3. Literature review
• Hotel reviews – the text
• Text usefulness (Liu and Park, 2015; Chung, Le, Koo, and Chung, 2017)
• Effects of language on review text valence (García-Pablos, Duca, Cuadros, Linaza, and
Marchetti, 2016)
• Intention to book (Tsao et al., 2015)
• Reliability of online reviews (Xiang, Du, Ma, and Fan, 2018)
• Machine Learning provided increasingly better insights
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4. Literature review
• Hotel reviews – the picture metainformation
• Presence of profile picture of review author increases perceived usefulness
(Liu and Park, 2015)
• Presence of picture in the review increased helpfulness of the review (Ma, Xiang,
Du and Fang, 2018)
• Pictures “can become a primary source of data for understanding the
form, meaning, and the process of photographic representation in
tourism” (Albers and James, 1988, 135)
• Photography for storytelling (Urry, 1990)
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5. Literature review
• Hotel reviews –
the picture content
• Assumed value to others
• Holiday Inn Orlando
Disney Springs Area
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6. Literature review
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7. Literature review
• Pictures uploaded to hotel review websites include “non-hotel”
pictures
• Cityscapes
• Landscapes
• Guests can learn more about the destination
• Study goal: understand the relationship between picture content and
review score
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8. Method
• 2016 TripAdvisor data; Orlando, Florida; Python and Java crawler
• 86,227 pictures – rating scores (1-5) for each
• 1-2 = negative, 3 = neutral, 4-5 = positive
• Residual Network (ResNet) (He, Zhang, Ren and Sun, 2016)
• 1,000 built in picture classes
• 152 layers
• Predicts ImageNet classes (Russakovsky et al., 2015) with 94.06% accuracy
• Classes were grouped
• Core hotel (bed, etc.)
• Non-hotel (buildings – churches, etc.)
• Both (kitchen and household items, etc.)
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9. Findings
• Positive – Neutral – Negative
• Reviews: 78 – 12 – 10
• Pictures: 84 – 9 – 7
• 114 classes not found in data (e.g. electrical watch, beagle)
• 71 classes with higher negative than positive scores
• 815 classes:
• Several with nearly perfect positive score (geography/landscape 92%,
buildings 89%)
• Pictures most often in negative reviews:
• Spiders (89%), other insects (71%), tools (61%)
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10. Findings
Image Class Negativ
e
Neutral Positive
band aid 41 7 19
barn spider 4 1 0
corn 0 1 9
day bed 177 412 3,341
diaper 20 4 51
gown 3 0 17
paper towel 79 15 90
pig/hog 2 0 2
radiator 70 22 22
sleeping bag 9 3 14
steel arch bridge 2 3 78
totem pole 7 13 218
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Picture class examples in
negative/neutral/positive
reviews (frequency
counts)
Hotel
Non-
Hotel
Both
Positive 14,918 39,091 18,333
Neutral 1,945 3,478 1,839
Negative 1,779 2,948 1,896
Total 18,642 45,517 22,068
Total % 21.6% 52.8% 25.6%
Picture distribution

11. Findings
Hotel % Non-Hotel % Both %
quilt comforter 23.45% lakeshore 18.87% patio terrace 20.62%
day bed 22.40% palace 4.08% fountain 12.89%
four-poster 11.85% seashore coast 3.72% dining table 8.72%
washbasin 8.95% restaurant 3.57% microwave 8.44%
shower curtain 5.25% medicine cabinet 3.13% sliding door 6.95%
Sub-total 71.89% Sub-total 33.37% Sub-total 57.32%
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Top 5 picture classes in POSITIVE reviews (in % of hotel/non-
hotel/both lists)
Hotel % Non-Hotel % Both %
washbasin 13.66% doormat 5.19% electrical switch 10.60%
quilt comforter 13.32% lakeshore 4.21% sliding door 5.17%
day bed 9.95% medicine cabinet 3.63% patio terrace 5.17%
toilet paper 9.67% bannister 2.75% paper towel 4.17%
shower curtain 9.27% dishwasher 2.10% lampshade 4.11%
Sub-total 55.87% Sub-total 17.88% Sub-total 29.22%
Top 5 picture classes in NEGATIVE reviews (in % of hotel/non-
hotel/both lists)

12. Discussion and conclusion
• Non-hotel related pictures are dominant
• A vote for the relevance of the shown items
• Relevant for decision makers >>> “get to know your stakeholders”
• Opportunity to increase guest satisfaction – positive/negative triggers
different responses
• Research outlook
• Class weights in positive/negative reviews
• 1,000 classes are a good start
• Limitation
• Exploratory study
• Only one destination
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13. Thank You!
Contact: florian@vt.edu
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