Location2Vec:
Generating Distributed Representation of
Location by Using Geo-tagged Microblog Posts
Yoshiyuki Shoji1[0000−0002−7405−9270] ,Katsurou Takahashi2[0000−0001−8418−8853] ,Martin J. Dürst1[0000−0001−7568−0766] ,Yusuke Yamamoto3[0000−0001−9829−6521] , andHiroaki Ohshima2[0000−0002−9492−2246]
1

Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara-shi,
Kanagawa 252-5258, JAPAN {shoji, duerst}@it.aoyama.ac.jp
2
University of Hyogo, 7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe-shi,
Hyogo 650-0047, JAPAN {ab18y501, ohshima}@ai.u-hyogo.ac.jp
3
Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu-shi,
Shizuoka 432-8011, JAPAN yamamoto@inf.shizuoka.ac.jp

Abstract. This paper proposes a method to represent the characteristics of a place (i.e., use of the venue, atmosphere of the area) by using
geo-tagged microblog posts around the place. It enables a vector representation of a location similar to the distributed representation of a termin Word2Vec. Our method uses a simple neural network that is trained
through the task of estimating the terms that appear in tweets posted
from the area. The effectiveness of our method is illustrated through anexperiment of a comparison of similar locations in Tokyo and Kyoto.
Keywords: Geo-tag · Social Sensing · Word2Vec · Social Media Analysis

1

Introduction

When we are visiting a certain location, we are concerned about its atmosphere.
It is difficult to guess how the atmosphere of a location will be, and it is moredifficult to search for locations with an atmosphere similar to that of a wellknown location. One reason of this difficulty is lack of data; how visitors felt
there or what visitors did in that place does not appear in official information
or traditional Web sites. Also, we often face the situation that we need to find alocation based on its usage or its atmosphere in daily life. For instance, when you
move to a new city, you may look for a coffee shop that has an atmosphere similar
to a familiar coffee shop in your previous home town. In such a situation, there
is no point in searching for a shop with a similar name, or to search for a shopwith a menu similar to the one at your familiar shop. It is more important tofocus on the environment around the shop, and how other customers are feelingat the shop. There are also marketing needs; if you are the owner of a thriving

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shop, you want to look for a place that has similar atmosphere to your current
shop’s location in order to open a second shop. What kind of shops will become
more popular in the area depends on the atmosphere or usage of the area.
Posts in social media are one of the most useful information sources to know
the atmosphere of a location. For instance, the contents of tweets in downtown
areas and residential areas are quite different. By analyzing them, we can findthe difference of atmosphere between those areas.
We propose a method of generating arbitrary dimensional feature vectorsof locations by using geo-tagged microblog posts. Every vector contains human
information such as how people feel about the location and what people did
there because it is generated from common daily social media posts. By using
such feature vectors as input to machine learning methods, or for direct vector
operations (e.g., similarity calculation, addition and subtraction), we can compare, analyze, or search for locations by atmosphere. For instance, similarity ofvectors is likely to be a good clue for information retrieval tasks that search
for a location with a similar atmosphere to a given location. It is also usable forclustering areas by their atmosphere, or for area visualization. As an application,
the vector is suitable to be used as input of machine learning methods, similar toembedding of terms. It will enable a more advanced social analysis of locations
by using modern machine learning techniques.
Our method uses a simple neural network to create a feature vector for an
object. This approach follows the basic idea of Word2Vec [15]. Word2Vec and its
derivative methods train their networks to estimate linguistic contexts of termswith those surrounding terms. Finally they use the weight of the hidden layer as
the feature vector of the term. The vector is called “distributed representation”
of the term, and it represents the meaning of the term. This approach is basedon the hypothesis “the meaning of a term is defined by the terms around it”. We
formulated a similar hypothesis: “the meaning of a certain location is defined by
the terms appearing around it”. Here, “terms appearing around it” is related tophysical distance. It means terms included in geo-tagged tweets posted close to
the location. Figure 1 shows an outline of the neural network that our method
uses. The weight of the edges from a dimension that represent a certain place
to the middle layer can be used as a feature vector of the place, in analogy with
Word2Vec.
The rest of this paper is organized as follows. Section 2 describes related work.
Section 3 explains our method named “Location2Vec”. Section 4 describes our
experiment and its results. In Section 5, we examine the experimental result. We
conclude with a summary of the key points in Section 6.

2

Related Work

The purpose of our method is sensing the use or atmosphere of a location fromsocial media posts by using a shallow neural network model. We discuss existingprior research related to our method from two view points; Social Sensing and
Word2Vec-like Methods.

Location2Vec
Classiﬁer

I

eat

lunch

3

on

Table 1. Locations in Tokyo andKyoto sampled for the experiment.
statistics
Location Matrix

L
Location ID

Fig. 1. Simple neural network
estimating terms appearing around a
given location.

Size of the Area
7,854m2# data in full dataset 256,289# data sampled
35,211
Sampling rate
14%
Average # tweets
33.09
Median # tweets
11Mode # tweets
0
Max # tweets
1995

There is a lot of previous research in the field of Social Sensing. Social media posts are used to detect real place information or events [13, 1, 12, 21, 7,
9]. Geo-tagged social media posts are good resources for subjective or empiricalinformation. Some of research uses geo-tagged posts to find physical phenomena
related to the location, such as earthquakes [19], weather events [6], intentionof moving [23], and so on. Our work is also an instance of a Social Sensing approach. Our method incorporates both term features and coordinates of tweets
into its algorithm.
There also exist some papers which expand topic modeling methods withlocation-based features. These methods use the physical distance as the relationship between terms or topics. Canh et al. [4] proposed an extended model of
LDA (Latent Dirichlet Allocation) named “Spatial LDA”to find regional communities. Kurashima et al. [11] proposed the “Geo Topic Model”, which is a new
topic model that uses information about users and locations to estimate topics. Ahmed et al. [2] proposed a hierarchical geographical modeling method of
combining location and text content in social networking services. Yin et al. [26]proposes LGTA (Latent Geographical Topic Analysis), which combines both
location-driven methods and text-driven methods to detect topics appearing in
geo-tagged social media posts. The method that we propose also calculates thefeature vector of a place. Our vectors will have properties similar to vectors
made by these topic modeling methods; containing subjective information of theplace such as usage, opinion or atmosphere. The main difference is that we chosea Word2Vec-based neural network method to make a vector. Our method canaccept various improvements of Word2Vec applications.
Word2Vec is a family of methods to generate distributed representations ofwords, proposed by Tomas Mikolov in 2013 [15]. Word2Vec has many derivative methods and applications. All of them have in common that they use ashallow neural network model. The most famous derivatives are Doc2Vec andParagraph2Vec proposed by Mikolov himself [16]. These methods are frequently
used for vectorization of words or documents. There exist many methods combining Word2Vec with other natural language processing methods in order toimprove performance and create additional applications (e.g., LDA2Vec [17]).

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Y. Shoji et al.

Nowadays, the Word2Vec-based approach is a popular embedding methodfor deep learning. There are many derivative methods which accept other kindsof data; not only terms or documents, but also graphs, multimedia data, andso on. RDF2Vec [18] creates distributed representations of graph entities. It
converts sub-graphs into sequential tokens. Node2Vec [8] is another Word2Vecbased graph vectorization method. A characteristic of this method is how to sample a node’s neighborhood feature; it uses random walk as a negative sampling
method. As an application for multimedia data, Madjiheurem et al. [14] proposed Chord2Vec, which converts musical chords to a feature vector. Alcorn [3]proposed a method named “(batter—pitcher)2Vec”, a unique derivative methodof Word2Vec, which vectorizes major league baseball players. Place2Vec [25] isalso a model which is very similar to our method. It estimates a location by
its surrounding locations. The main difference is that we use physical distanceinstead of conceptual distance such as co-occurrence of terms or hop count in agraph.
There are some methods applying Word2Vec to social media posts. Dhingra et al. [5] proposes “Tweet2Vec”, an approach for vectorizing non-geo-tagged
tweets. It uses hash tags in Twitter as an objective of the estimation. Thereis another algorithm called “Tweet2Vec” [22] that does not use hash tags, but
adopts a method similar to LSTM (Long short-term memory) and its auto encoder. Word2Vec is typically used to summarize sentiments and opinions fromsocial media posts. One of the most common usages of Word2Vec in social media
analysis is vectorization of the posts in the same way as other topic modelingmethods or dimensionality reduction methods. Wang et al. uses Word2Vec to
estimate the usage of the area from geo-tagged posts from Sina Weibo [24]. Sekiet al. [20] also uses Word2Vec to estimate the sentiment of the area by analyzing
Twitter posts which are posted by users who live in the area. These methods
use standard Word2Vec to vectorize social media posts by using co-occurrence ofterms. Our method incorporates geographical factors into the calculation itself.

3

Location2Vec Algorithm

In this section, we describe the details of our method named “Location2Vec”.
The novelty of our method is the data used in the estimation. Word2Vec uses surrounding terms and a central term as input and output of their estimation. Our
method uses the location instead of a central term, and uses the terms appearing in the tweets posted in the area around that location instead of surrounding
terms.
3.1

Training Data Creation

Our method generates a feature vector of a location from geo-tagged tweets
around the location. This idea can be considered as an analogy of the concept
of “context window” in Word2Vec. That is, it uses “terms that appeared in a

Location2Vec5geo-tagged tweet that was posted within 10 meters from the location” insteadof “terms that appeared within 10 words around a term”.
A specific location, such as a shop, an institution or a restaurant has coordinates represented as longitude and latitude. The most primitive method is to use
tweets posted at a location within a radius of n meters from the target venue.
We calculated the distance between the venue and the location of a tweet by the
Hubeny formula. Although it is a reasonable method for representing a place byusing the tweets around it, there is a problem in the training of the network.
Sometimes several venues are located at the same coordinate when they are ina multistory building or in a commercial complex. In addition, in dense areas,
different kinds of venues will share their surrounding tweets. The training of theneural network often fails under these circumstances. For instance, it happensthat the classifier has to estimate different outputs from the same input. A reasonable way to eliminate the duplication of tweets is isolating areas uniformly
by a grid. In this case, instead of a certain venue, the area is expressed using
the tweets included in the grid. The size of the grid will have the same effect as
the window width of Word2Vec. A fixed size grid will work well if the densityof tweets in the dataset is homogeneous. Yet, it should be considered how to
divide the map. For example, by changing the cell size according to the densityof tweets, or using area division methods such as a Voronoi diagram. There is apossibility of improving the accuracy of estimation.
3.2

Calculation

Our method follows the skip-gram model of Word2Vec. When a document ID isgiven, the method estimates the list of words included in the given document.
Our algorithm uses physical areas on the earth, separated by certain conditions,in the same way the original algorithm uses documents. It uses words included
in tweets actually tweeted within a certain area instead of words in a certain
document.
The model is shown in Fig. 1. The input is a square matrix consisting of onehot vectors. Dimensions of each vector and the number of vectors are similar
to the number of locations in the dataset. The output is a set of vectors that
represents which terms were used in each location. The dimension of each vector
is similar to the number of terms used in the dataset, and the number of thevectors is similar to the number of locations. Thus the m-th dimension of then-th vector is 1 if the m-th term appeared in posts around the n-th location.

4

Experiments

We evaluated our method with an application similar to analogy-based information retrieval. The aim of this experiment is to verify the usefulness of our
method for an actual location search system.
We performed an evaluation task that analyzes the similarity between locations in two different cities. This task is related to analogy-based search. Analogybased search [10] is an Information Retrieval model which accepts an example in6

Y. Shoji et al.

a known domain as a query, and returns similar entities in an unknown domain.
For instance, if a user wants to know “The location in Singapore that is similar to
the Statue of Liberty in New York”, then system should return “Merlion”. Thiskind of search model is reasonable for search tasks with ambiguous informationneeds, such as the atmosphere of a place.
We conducted a experimental task that discovers pairs of areas with similar
atmosphere in Tokyo and Kyoto in Japan. The reason why we choose Tokyoand Kyoto is because both are large cities in Japan (first and fourth). Therefore,
it is easier to collect a large number of geo-tagged tweets, sufficient to train aneural network-based machine learning method. In addition, it is easy to findevaluators for the experiment who know both Tokyo and Kyoto.
4.1

DatasetTo compare the two areas, we collected tweets posted in the two cities, andextracted some of them. Tweets were collected from April 2016 to March 2017from the Twitter Streaming API. Table 1 shows the statistics of the dataset. Thisdataset contains sampled foursquare venues located in Tokyo or Kyoto. First, we
extracted venues in the rectangles of Tokyo area and Kyoto area. As both Tokyoand Kyoto are big cities, 31 percent (256,289 / 826,266) of the venues in Japan
were included in the dataset. To reduce calculation cost, we randomly sampled 14
percent of the venues from the whole dataset. Next, we extracted tweets posted
around each venue. We set a radius r = 50m. Tokyo and Kyoto are high density
cities. Our dataset contains many venues which share surrounding tweets withother venues. Finally, we cleansed data, as follows. Our dataset contains many
useless geo-tagged posts; such as posts by bots, “Check-in” posts of geo-social
networking services, and replies for friends. We removed tweets that meet any
of four conditions below, and keeping 5.15 percent (1,165,109 / 22,640,460) of
tweets:– tweets that include URIs, or other users’ IDs (i.e., replies),– tweets that do not contain any Japanese characters,– tweets that contain terms characteristic for bots, and– tweets by a user who posted more than 20 times in the same location.
4.2

Methodology

We analyzed pairs of locations in Tokyo and Kyoto. Areas have their own atmosphere, such as atmosphere of exclusive residential area, atmosphere of desolate
town and so on. First of all, we generated 200 dimensional vectors for each venue.
We used “keras”, the python TensorFlow library for neural network calculation.
We used the default parameters of the library for estimation.
Next, we compared pairs of locations by hand. We created a ranking of pairsconsisting of a location in Tokyo and a location in Kyoto by their similarity. Euclidean distance was adopted as the similarity measure in 200 dimensional vector
space. Our dataset contained 31,772 venues in Tokyo, and 3,439 venues in Kyoto.

Location2Vec

7

Table 2. Top 10 “similar” pairs in Tokyo and Kyoto. The pair in each line was estimated as places that have same atmosphere .
Place in Tokyo

Place in Kyoto

Dining district in a small station Peaceful residential area
Dining district in high-end area
Dining district in high-end area
Commercial building in big station Commercial building in midsize station
Dining district in a old town
Out of downtown
Road-side in commuter town
Residential area in suburb
Backstreet of shopping street
A famous temple
University
Event areas near Kyoto station
Luxury office area
Road-side in suburbs
Station in a commuter town
Interchange in suburban area
Residential area along a river
Residential area next to imperial palace
Table 3. Top 10 “dissimilar” pairs in Tokyo and Kyoto. The pair in each line wasestimated as dissimilar combination.
Place in Tokyo

Place in Kyoto

Bar district
Shrine in a mountain
Shopping mallCollege town
Dining area in deserted station
Famous shrine
In front of famous market
Marine museum
Subway station in residential area Famous shrine
Suburban residential area
History museum
Anime vocational school
Marine museum
Park
Center of downtown
Station in old town
Commercial complex
Dining area in deserted station
Guest house at mountainWe calculated the similarity of 109,263,908 pairs. Two evaluators analyzed the
top 100 pairs and bottom 100 pairs one by one. They discussed locations’ atmosphere, other venues around the location, usage of areas, and analyzed tweets
around there.
4.3

ResultWe compared pairs of two locations in Tokyo and Kyoto. The ranking of pairs
is shown in table 2. We translated the concrete location to a short description.
We were able to find some pairs of dining districts at the top of the ranking.
People often tweet at restaurants; they post what they ate, and the evaluationof the restaurant. In dining district, it is likely to easily find clues to calculate
similarity. Our method also finds many pairs of residential areas. In these areas,
we were able to find daily tweets posted by residents. Through the top part ofranking, pairs of suburban residences frequently appeared. The reason may bethat they are just numerous in our dataset, and all of them are similar to eachother. Conversely, Table 3 represents the ranking of the most dissimilar pairs oflocations. It contains many incomprehensible combinations of locations.

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Discussion

There is some room for improvement in the proposed method. First, a vector
generated by the proposed method is affected by the amount of documents forthe target place. It was assumed that tweets within a range of 50 meters fromthe target place characterize the place itself. However, if the number of tweets
is too small, we cannot calculate the characteristics of the location well. Since
the appropriate distance would be different for each area, we should consider
dynamic distance configuration.
The quality of the tweets is also an important factor. We deleted unnecessary
tweets such as ones by bots. However, there are still many tweets that do not
represent features of the target place (e.g., daily reports, everyday conversations
between users). A clean dataset is important for good results, even if strong data
cleansing reduces the amount of available training data.
Applications using Location2Vec can also be improved. We conducted anexperiment to discover similar places among different regions. We calculated
the distances of vectors at two places from different regions to realize such an
application. The vector of a certain place in Tokyo vaguely contains the featureof Tokyo as a whole. If we can acquire the characteristics of Tokyo as a vector,
we can express the unique features of the target place in Tokyo more clearly bysubtracting the vector representing the characteristics of Tokyo from the vector
at the target place. One way to obtain such a vector is to acquire the average
vector at every place in Tokyo in the proposed method. Alternatively, in machinelearning, it is conceivable to use a neural network that takes regional hierarchy
into account.

6

Conclusion

We proposed a method of generating a feature vector of a place from tweets
posted around the place. Since the feature vector is made from social media
data, this vector may contain subjective information such as use of the venue
or atmosphere of the area. We followed the Word2Vec algorithm and its basic
idea. Our method uses a simple neural network which is trained through theestimation of terms that appear in tweets posted around the place.
We conducted an experiment: a case study of analogy-based search by using
geo-tagged tweets in Japan. It showed the possibility of our method for social
analysis.

Acknowledgements
This work was supported by JSPS KAKENHI Grant Numbers JP18K18161,
JP17K17832, JP18KT0097, JP16H02906, JP16H01756, JP17H00762, JP18H03243.

Location2Vec

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