Neighborhood’s Sentiment Cloud:Visualization of leisure through text analysis○ Danilo Eidy Miura*1 and Shunsuke Miyahara*2*1 Research Assistant, School of Engineering, University of Tokyo, M.Eng.
*2 Lecturer, College of Industrial Technology, Nihon University, Dr.Eng.
Keywords: Urban planning, Urban leisure, natural language processing, data visualization.

1. Introduction
In Urban planning, the experience of the future users anddwellers has been noticed as an important dimension for theattractiveness of a certain area. Factors such as Sensorial
experience, leisure, quality of life indicates how users perceive
their experience.
A problem for urban planners is the access to the subjective
experience of users. But with the advent of Big Data and AI
technologies, it becomes possible to assess the experiences
through the history of users in a certain area.
This paper presents an application o sentiment analysis of users’
reviews around the 5 busiest stations in the world.

Shinjuku, Shibuya, Zurich Hauptbahnhof, Gare du Nord (Paris),and Pennsylvania (New York).
3.2. Data Collection
In order to investigate the leisure activity in a certain area, weaimed the reviews data from users about places provided online.
The data were collected through Google Maps API, which is an
online service that gives access to data of Google Maps andreviews of places.
Places in the area of 2-kilometers radius around the busiest
stations were identified by their names, coordinates. And for eachplace, we extracted total number of reviews and the contents of
the latest 5 reviews.

2. Related Work
Previous studies show the relevance of leisure in urban areas toquality of life and sensorial experience of in a certain area [1]. In
addition, it has its relevance to regional tourism and urbanplanning. Therefore, leisure constitutes one of the relevant factors
for the urban regeneration.
Taking advantage of the grown Big Data, previous research
explored the Sentiment Analysis of data from users of social
networks, showing the potential use of Natural Language
Processing to forecast the overall sentiment of the urban area
[3,4,5,6].
2.2. Natural Language Processing and Data Visualization
Sentiment Analysis is a subfield of Natural Language Processing
which aims to recognize sentiment, feelings and opinion fromnatural language text [7]. Sentiment Analysis has been developed
in several different approaches [8,9] with shared techniques withtext classification. In this current study, we approach thesentiment of users by the simple rule-based analysis, in which,basically, adjectives are classified by being positive, neutral ornegative.
3. Research Method
This study aimed to demonstrate the analysis of sentiment ofusers of certain areas expressed on online service where users canpost their reviews. After analysis of the textual data, the results
are shown in visualization in form of heat map, which provides
special sense on geographic map and high resolution of thesentiment, using heat marks.
3.1. Scope
We define leisure as the activity out of work, job hunting orhousing activities. In this study, leisure in consideration are theactivities at places around stations that users of Google Maps
posted their reviews. The geographic locations were defined as
the regions around the busiest stations of the world [11],日本建築学会情報システム技術委員会
第 42 回情報･システム･利用･技術シンポジウム論文集， 78-81 ， 2019 年 12 月，東京
Proceedings of the 42nd Symposium on Computer Technology of Information ，Systems and Applications ， AIJ ， 78-81 ， Dec. ， 2019 ， Tokyo

Figure 1 – Example of User’s review
3.3. Procedures of Sentiment Analysis
We used the lexicon-based method to analyze the sentiment of
the reviews. In this method, the text is split into words and eachword is searched in a dictionary that classifies the word as
positive, negative or neutral.
Normally, in the second step, the sentiment of the full text iscalculated according to the sentiment of the words in the text. But
instead of overall calculation, we kept the list of positive andnegative words. We use the bags-of-words to make interpretation
in a qualitative analysis.
With the purpose of qualifying the particular sentiment that
reviewers have about each station, we divided the words
according to their representativeness to each station. It means aword is assigned to a station if this word has higher relative
frequency to this station than the others.
3.4. Data Visualization
Collected data were visualized in two different graphs. The
number of reviews is visualized in Heat Map in geographic map
to provide the sense of concentration of leisure in the area aroundthe stations. And the words with sentimental change werevisualized in Word Clouds, where the size of words represent
their frequency, the larger it is more frequent in reviews.

－ 78 －

4. Results
4.1. Collected Data
The data shows a large number of places around the busiest
stations in the radius of 2-kilometers, with the average of about
226 places per station, and the average of total number of reviews
per station is about 100,600, with a discrepancy of the number ofreviews around the Pennsylvania station, New York.
Table 1 - Total number of collected data per station
StationNo. of Places
Total no. ofreviews
Shinjuku
194
53,761
Shibuya249
59,338
Zurich221
56,286
Gare du Nord
233
61,601
Pennsylvania
234
274,774
This discrepancy was treated before the visualization, defining
parameters to allow a “fair” comparison of the resultedvisualization.
4.2. Distribution of types of places
The reviewed places were classified in 5 categories: food,shopping, lodging, service, and others. Google Maps API
provides the types of each place registered and it could beautomatically assigned to the place.
Food: places that primarily offers food or drink as service.
Shopping: Commercial establishments.
Lodging: Hotels, hostels, and rental houses.
Service: Services except food and lodging.
Other: Any other than previous and unclassified places.

Shibuya

ZurichParis

New Yorkfoodshopping
lodging
serviceotherShinjuku

Table 2 – Distribution of types of places per area

113
27
23
14
15

114
87
61725

37
84
32
31
28

30
30
119
29
22

21
27
60
55
67

We can consider food and shopping as main categories of leisure,while lodging just indicates travelers staying in the area, andservice may not be considered as leisure activity, but neededservice.
The representativeness of the distribution of types of places isshown in Figure 2. And the comparison reveals dominance offood, shopping and lodging in each area.
Shinjuku: Food dominant
Shibuya: Hybrid Food-Shopping dominant
Zurich: Shopping dominantGare du Nord: Lodging dominant
Pennsylvania: Lodging dominant (not considering other)

報告 H99Figure 2 – Comparative representativeness of types of places
The dominance of the type of place in a certain area indicateswhat kind of review we expect to find more and the main purposeof the leisure activity. A possible explanation to the difference ofrepresentativeness of lodging type reviews is the predominanceof leisure activity of tourists or locals.
4.3. Data parameterization
Due to the discrepancy of the number of reviews of aroundPennsylvania station, which has about 5 times the average of theothers, it was decided to parameterize with a higher parameter,
defined by a scale of 20 points between minimum and maximumvalue of number of reviews of places around each station. Doingso, each visualization shows the maximum level of heat at 20degrees and minimum of 1 degree. This treatment allows us tosee the concentration of the leisure activity around the stations
with the same parameter of colors of heat.
The higher parameters defined for the Heat Map visualization
allow us to see the area of the 5 stations with the same color
parameter but it limits the sense of accumulated reviews forcomparison. In addition, Google Maps API provides the total
number of reviews per place in the history without providing theperiod that that place is registered in the database. That may
explain the discrepancy of number of reviews in New York,
which may have been registered for longer time than the otherstations, accumulating more reviews.
4.4. Positive and negative words
The words from the reviews were collected and classified as
positive and negative sentiment charge. Neutral words wereignored.
Table 3 – Top 10 words with sentiment
Good words (frequency)
Bad words (frequency)
Good791
Bad100Great548
Pay76
Nice504
Worst50
Friendly362
Hard48
Like317Rude46
Clean313
Leave
45
Best
212
Problem
39
Recommend
197
Avoid37
Well
175
Unfortunately
33
Helpful
167
Noisy
32
Table 3 shows the overall counting of good and bad words of allareas together. Positive words have superior frequency andvariety of words (831 different positive words vs 621 different
negative words).

－ 79 －5. Discussions

References

5.1. Limited Data1)

Google Maps API presented two limitations to the datacollection. One is the number of places retrieved per query andthe other is limited reviews per place.
Each query of places in Google Maps API returns the maximumof 60 places. To cope with it, the data was collected in a series ofqueries centered on the station with increasing radius. Starting
the first query with 100-meter radius and ending with a querywith 2000 meters radius. It implies in a higher chance to retrieve
places closer to the station.
Each query of reviews’ contents in Google Maps API returns only
the last 5 reviews. It implies in a limited textual data that may notproperly represent the common opinions of users of places
around the stations in a long term and may change from time totime.
5.3. Comparison of leisure activity around the stations
The Heat Map shows the concentration of reviews around thestations.
We can make the observation of concentration-dispersion ofreview places around stations and compared than with the shapeof the roads. We can infer the how the proportion of four-way
intersections may interfere in the concentration of leisure activity.

Lloyd, K., & Auld, C. (2003). Leisure, public space and qualityof life in the urban environment. Urban policy andresearch, 21(4), 339-356.
2) Lian, H., & Yue, P. (2019, August). A Review on Leisure CityEvaluation Index Systems and Model. In 2019 5th International
Conference on Social Science and Higher Education (ICSSHE
2019). Atlantis Press.
3) Weerdenburg, D. V. (2017). Web Data and Urban Space.
Investigating Urban Leisure Activities using Web based Semantic
Enrichment (Master's thesis).
4) SentiHood Targeted Aspect Based Sentiment Analysis Dataset forUrban Neighbourhoods5) Kovacs-Gyori, A., Ristea, A., Havas, C., Resch, B., & CabreraBarona, P. (2018). # London2012: Towards Citizen-Contributed
Urban Planning Through Sentiment Analysis of Twitter
Data. Urban Planning, 3(1), 75-99.
6) Bertrand, K. Z., Bialik, M., Virdee, K., Gros, A., & Bar-Yam, Y.
(2013). Sentiment in new york city: A high resolution spatial andtemporal view. arXiv preprint arXiv:1308.5010.
7) Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). Theevolution of sentiment analysis—A review of research topics,venues, and top cited papers. Computer Science Review, 27, 1632.
8) Mirończuk, M. M., & Protasiewicz, J. (2018). A recent overviewof the state-of-the-art elements of text classification. Expert
Systems with Applications, 106, 36-54.
9) Araque, O., Corcuera-Platas, I., Sanchez-Rada, J. F., & Iglesias,
C. A. (2017). Enhancing deep learning sentiment analysis withensemble techniques in social applications. Expert Systems withApplications, 77, 236-246.
10) Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment
analysis algorithms and applications: A survey. Ain Shams
engineering journal, 5(4), 1093-1113.
11) 5 of the Busiest Train Stations in the World. Retrieved September,
1st, 2019 from https://www.triptrivia.com/5-of-the-busiest-trainstations-in-the-world/Figure 3 – Heat Map of Shinjuku and New York (concentration)
Observing the example of Shinjuku, where there is a large variety
of intersections including dead ends, we see an intenseconcentration of reviewed places close to the station. In contrast,planned area around Pennsylvania shows relative dispersion ofreviewed places.
5.4. Particularity of sentiments about the stations
The word clouds show the particular good and bad words for eachstation. These words reveal some of the relevant points for eacharea. The points may refer to some issues or specialty of theplaces which is reinforced by the common antonyms present in
the pair of good-bad words. For example, issues such as betterworst in Shinjuku, recommended-disappointed in Shibuya,friendly-unfriendly in Zurich, clean-dirty in Paris, great-terrible
in New York.

報告 H99－ 80 －

Shinjuku
Shinjuku

ShibuyaShibuya

ZurichZurichGare du Nord

Gare du Nord

Pennsylvania

Pennsylvania

4.3. Compared results (Heatmap)

4.4. Compared Results (Word Cloud)

ANNEX – COMPARISON OF VISUALIZATIONS OF HEATMAPS AND WORDCLOUDS OF AREAS

報告 H99－ 81 －