Semantic Analysis of the Imperial Topic: Case of St. Petersburg

Abstract

Tourism products' variety engenders a correspondingly large number of approaches to their promotion. One popular angle for promotion, applicable in a handful of cities, draws on the destination's imperial heritage. However, the patterns of using the imperial theme for tourism marketing vary in different capitals. This study is the first in a larger project aiming to study the former imperial capitals (St. Petersburg, Istanbul, Berlin, and Vienna). Our overall goal is to understand, first, how city representatives and travel agencies narrate the "imperial" past and turn it into a competitive advantage for the city, and second, how tourists react to such narratives. To collect and analyse the data, web scraping and text mining techniques are used. In this article, the first results of our study using the case of St. Petersburg are presented.

Full text

e-Review of Tourism Research (eRTR), Vol. 16, No. 2/3, 2019
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Aleksei Gorgadze
National Research University - Higher School of Economics
Valery Gordin
National Research University - Higher School of Economics
Natalia Belyakova
National Research University - Higher School of Economics
Semantic Analysis of the Imperial Topic: Case of St. Petersburg
1
Tourism products’ variety engenders a correspondingly large number of approaches to their
promotion. One popular angle for promotion, applicable in a handful of cities, draws on the
destination’s imperial heritage. However, the patterns of using the imperial theme for tourism
marketing vary in different capitals. This study is the first in a larger project aiming to study
the former imperial capitals (St. Petersburg, Istanbul, Berlin, and Vienna). Our overall goal is
to understand, first, how city representatives and travel agencies narrate the “imperial” past
and turn it into a competitive advantage for the city, and second, how tourists react to such
narratives. To collect and analyse the data, web scraping and text mining techniques are used.
In this article, the first results of our study using the case of St. Petersburg are presented.
Key words: imperial capitals, semantic analysis, digital footprint analysis
Aleksei Gorgadze
Department of Management
St.Petersburg School of Economics and Management
National Research University - Higher School of Economics
3A Kantemirovskaya street, St. Petersburg, Russia
Phone: +7 (911) 834-88-02
Email: agorgadze@hse.ru
Valery Gordin
Department of Management
St.Petersburg School of Economics and Management
National Research University - Higher School of Economics
3A Kantemirovskaya street, St. Petersburg, Russia
Email: gordin@hse.ru
1
The results of the project “Cultural and event activity as development factor of revitalized
territories”, carried out within the framework of the Basic Research Program at the National Research
University Higher School of Economics (HSE) in 2018, are presented in this work.

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Natalia Belyakova
Department of Management
St.Petersburg School of Economics and Management
National Research University - Higher School of Economics
3A Kantemirovskaya street, St. Petersburg, Russia
Email: nubelyakova@hse.ru
Aleksei Gorgadze is a postgraduate student in the Department of Management at the National
Research University - Higher School of Economics and the lecturer of the Text Mining
course. His research interests include event management, tourism studies, text mining, and
social network analysis.
Dr. Valery Gordin is a tenured Distinguished Professor of Management at the National
Research University - Higher School of Economics and the academic supervisor of the
Master’s program “Cultural and Event Tourism Management”. His current research focuses
on the economics of cultural tourism, as well as on the field of management in cultural and
creative industries.
Prof. Natalia Belyakova is an Associate Professor of Management at the National Research
University - Higher School of Economics and the regional marketing and PR Director at
Domina Holding.

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Introduction
St. Petersburg is one of the world’s few large cities whose appearance was entirely
planned. From its founding in 1703, the city was preordained to become the capital of the
Russian Empire – the status it enjoyed for three hundred years. But even a century after
relinquishing it, the city’s imperial spirit is still alive. Tour operators and the city authorities
promote tourism products related to the city’s imperial past. But how effective are these
efforts?
Traditionally, the imperial theme has been prominently present in tourist-oriented
English-language media materials representing the city. The destination’s main connotative
marker, which defines it on the international tourism market, is “the capital of the Russian
Empire.” The focus of our attention in this paper, however, is not on media materials as such,
but rather on user-generated content featuring the “imperial” marker, as well as on the
analysis of this marker’s representativeness and weight in the overall body of associations
linked with Petersburg that were prompted by the commenters’ own experience.
At the same time, we, as researchers, are conscious of the close links between the two
datasets. It is impossible to neatly separate media-induced and experience-based images of
Petersburg – if only because the commenters themselves do not always register the media’s
influence on them (and sometimes even deny it).
Some of them are consumers of “official” content, which represents Petersburg as a
destination for cultural and historic tourism; others are forming their opinions in a tentatively
independent manner. Even the latter group’s judgement, however, is affected by the
previously assimilated patterns in perceptions of Russia (stereotypes).
Without putting “imperial tourism” into a separate category, we analyse the extent to
which the “imperial” discourse (a conglomerate of established perceptions of St. Petersburg
in the media space) is assimilated (read, reiterated) by consumers. Using semantic analysis,
we probe the following problem: how effective was the imperial rhetoric used in the city’s
positioning and promotion vis-à-vis the tourists who have visited the city? Our starting
assumption is that the presence of “imperial” connotative terms in travellers’ reviews
testifies, at a minimum, to the users’ familiarity with the concept, as well as to their effort at
its validation (by comparing the induced image with one’s own experience), the result of
which may be either acceptance and assimilation or rejection.
The historical legacy of a place can be used both in creating formalized brands and
in forming less structured “association clouds,” as well as for rank-optimization with an eye

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toward promoting pre-existing sub-brands (sites of cultural heritage, museums, etc.).
Pioneering research by Ashworth and Page in the early 2010s (Kavaratzis et al., 2010 &
Ashworth et al., 2011) lay the foundations for studying the “historic city” as an important
concept in city tourism – including studying how residents, tourists, and businesses are
influenced and informed by cities’ historical heritage. Currently, this topic is debated in
research publications on destination branding and marketing (for a brief overview, see
Scaramanga, 2012 and Evans, 2015). Nevertheless, the issue of perception of “historical”
information by tourists – how in demand it is, whether actual experiences match the
expectations, and how much it affects the final impression of a country – is typically left
outside the research scope. Our research is meant to bridge this gap.
The purpose of this study is to assess the extent to which representatives of the city
administration and tour operators call attention to the imperial past as a competitive
advantage for the city and compare it with the response that these “imperial” - themed
messages elicit in tourists.
The object of the study are the descriptions and reviews of sights, museums, and parks
of St. Petersburg posted on official web city pages, as well as reviews posted on the
TripAdvisor web site (with all data in the English language),
User-generated content (UGC), such& as reviews on TripAdvisor, is an important
source of information about consumer preferences. Digital footprint analysis is a relatively
new, but very promising field in tourism research. Numerous studies have been devoted to
the behaviour of tourists in the digital space, including the study of multifunctional tourist
spaces (Salas-Olmedo et al., 2018), the analysis of Internet users’ search queries (Dergiades
et al., 2018), the assessment of levels of satisfaction with hotel services by business travel
representatives (Boo et al., 2018), the study of sentiment analysis (emotional colouring) and
topic modelling of tourists’ reviews (Godnov et al., 2016), the study of digital footprint
efficiency as an indicator of tourism demand (Önder et al., 2016), the study of how perceived
hotel “value dimensions” are connected with the hotels’ categories (Kaspruk et al., 2017), etc.
Methodology
This study was set within the context of St. Petersburg – Russia’s second-largest city
and the former capital of the Russian Empire. The design of the study involves comparing
two corpora of text data: the supply side (descriptions of tourism products on the official city
web sites and the web sites of travel agencies) and the demand side (tourists’ reviews on the

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TripAdvisor web site). Thus, the methodology for data collection and analysis involves two
basic components:
1. Analysis of the creation and promotion of the “imperial” tourist product.
This component analyses web pages of the city administration’s official
representatives, as well as information about tourist products on travel web sites. In order to
select the relevant web page data, we compiled a list of keywords to use in Google searches.
For each search query, we selected the top 20 links to websites. Using scripts written in the R
language, we collected the descriptions of tourist products (in the English language) from
these sites. The resulting text data was processed and analysed using text mining techniques.
In order to assess the degree of use of imperial words in tour products, we generated a
list of key-words/indicators, which were searched and counted. Additionally, we considered
the average share of the imperial words use in each site’s description.
2. Analysis of the tourism products’ perception as “imperial” by consumers.
This component’s aim is to assess how much consumers of the tourist product really
perceive and respond to the “imperial” branding when visiting the city’s tourist sights. For
this purpose, we collected all English-language reviews of sights, parks, and museums of St.
Petersburg posted on the TripAdvisor website. We then searched and analysed the imperial
keywords that were used in the previous step of our analysis.
Web scraping was conducted in June, 2018. We collected the text data from 64 web
site belonging to the city administration and the city’s travel agencies, and 8,354 reviews of
St. Petersburg sights on the TripAdvisor website.
Data preparation involved several steps. First, we conducted lemmatization to
transform the words into their infinitive forms. This method is superior to stemming because
it doesn’t cut the ending and preserves the word’s sense. For this purpose, we used the R
package “textstem”. We then removed all stopwords using the list of English stopwords
contained in the R package “stopwords”. We also removed the punctuation, numbers, and
extra white spaces and made all letters lowercase. After tokenization, we built the document-
term matrix (documents indicate the reviews or texts of travel agent websites) for conducting
our analysis.
We used several techniques from text mining and basic statistics in order to compare
the supply side and the demand side. First, we used the t-test to identify significant
differences in the average share of imperial words used. In other words, we first compiled a
glossary of terms that identify imperial topics, then calculated these words’ share in each

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document (the number of imperial words divided by the total number of words in the
document) and finally compared these indicators’ means using Student’s t-test.
However, statistical methods based on the assumption of a normal distribution are
invalid in text mining (Dunning, 1993). Therefore, we updated our analysis with parametric
statistical analysis using the log-likelihood (G-squared) and the log odds ratio for the two
sub-corpora (Bradley, 1968 & Mood et al., 1974). The log-likelihood is a statistical
significance measure which shows how much evidence we have for a difference between two
corpora. The log odds ratio tells us how big or important a given difference is (Hardie, 2014).
In other words, it is an effect-size statistic, not a significance statistic.
Data analysis, results and discussion
Text data description
As previously noted, the data consists of two parts: the supply side (texts from official
city representatives and travel agencies) and the demand side (review texts). It is reasonable
to assume that the texts in these groups will have different semantic structures. In the first
case, we are dealing with longer textual descriptions of tourist products, written in a
professional language. User reviews, on the other hand, are relatively short and use everyday
language. The sub-corpora are described in Table 1. Nevertheless, if we compare the top
words in these two selections, we observe remarkably similar patterns (Fig.1 and Fig. 2):
Figure 1. Wordcloud of words in official
representatives’ and travel agencies’ web
sites. The size of each word indicates the
term’s frequency.
Figure 2. Wordcloud of words in reviews.
The size of each word indicates the term’s
frequency.

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Table 1: Description of the sub-corpora
Construct
Number of
documents
Total word
count
Unique word
count
Supply side (official representatives
and travel agencies)
64
139 115
11 802
Demand side (reviews)
8 354
267 891
10 342
Student’s t-test
We compared the average use of the “imperial” words in tour products (a channel for
promoting the imperial theme) and in tourists’ reviews on the TripAdvisor website. The t-test
showed a significant (p-value = 0.01411) difference in the mean (0.0047 & 0.0029) of these
two (independent) samples. This indicates that the prevalence of the imperial theme in the
tour products is, on average, higher than in the reviews.
Log-Likelihood (G-squared)
To establish a more relevant distinction in the perception of sights by tourists and tour
agents, it is useful to look at the likelihood of the appearance of each term. The log-likelihood
compares the occurrence of a term in two corpora to determine if it shows up more often or
less often than expected. This method is accurate even at low frequencies (Dunning, 1993).
Figure 3 shows the most relevant terms for the supply and demand side based on the
log odds ratio. We observe, for example, that tourists used words like “wow”, “toilet”,
“pocket”, “cloakroom”, “worthwhile”, “disappoint”, “glad”, etc. more often.
There are several types of narratives at play here. The first reflects the emotional
aspect of the visit. It’s hardly surprising that tourists should be more concerned about the
availability of basic infrastructure and that they may give advice on possible service
improvements. In contrast, the official representatives and the travel agencies focus on the
types and names of sights (“monastery”, “mariinsky”, “mikhail”, “portico”), and on those
terms that are specific to the tour route (“literary”, “drawbridge”, “industrial”, “cemetery”).

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Figure 3. Log-likelihood ratios of frequencies by Supply side and Demand side.
Our research is aimed at studying the imperial factor. Therefore, we consider the use
of such terms separately. First, we selected the terms related to the imperial topic. These are:
“imperial”, “emperor”, “empire”, “tzar”, “czar”, and “tsar”. The term “tsar” is a title that the
Russian monarchs often used in Russia. Etymologically, this title is derived from the Latin
title for Roman emperors, “caesar” (Vodoff, 1978). However, there are different
transcriptions of this term: “tsar” (the most common), as well as “tzar” and “czar”. For our
purposes, we combined all these words into a single term and used it to calculate the log-
likelihood. Despite the fact that the selected terms are used more in tourist reviews, they are
more typical for travel agencies (p < 0.0001).
Table 2: The statistics of united Imperial words
Term
Supply
side
Demand
side
G-squared
Log Ratio
imperial words
599
692
109.96***
-0.8563
*** - p < 0.0001; critical value = 15.13
We also considered these terms separately. Table 3 shows the distribution of these
terms, along with some statistics. All the words except “tsar” demonstrate a significant
difference (p < 0.0001 and p < 0.05 for the term “czar”) in the supply and demand sides. The
terms “imperial”, “emperor”, “empire” are more typical of texts by official representatives

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and travel agencies. Tourists use the words “tzar’ and “czar” more frequently. This leads us
to the hypothesis that international tourists see the “imperialness” of Russia in Eastern/Asian
terms.
Table 3: The distribution of Imperial words
Term
Supply
side
Demand
side
G-squared
Log Ratio
imperial
185
100
126.42***
-1.9522
emperor
157
77
118.11***
-2.0925
empire
46
19
40.14***
-2.3403
tzar
7
56
15.61***
1.9354
czar
36
116
5.51*
0.6234
tsar
168
324
0.72
-0.1171
*** - p < 0.0001; critical value = 15.13
* - p < 0.05; critical value = 3.84
The methodology and the results of this study can be applied to place brand-making using
UGC. Our results can be used by city authorities and travel agencies for the creation of tourist
products in order to help them to better represent the city’s brand on their websites.
Future plans
In parallel to this study, we are conducting a comparative analysis of the use of imperial
themes in other former imperial capitals (Istanbul, Berlin, Vienna). Comparison of the results
between these cases will allow us to test the hypothesis which states that the active use of the
imperial theme by travel agents does not result in the commensurate amount of relevant
feedback on tourists’ part. Moreover, we plan to apply modern accurate text mining methods
such as LDA and LSA. This will allow us to find hidden phenomena and dependencies in our
database.
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Acknowledgements
We would like to express our gratitude to Ilya Musabirov and Anastasia Kuznetsova for their
assistance in data collection,&Slava& Borisov& for& his& help& in& preparing& this& article& for&
publication, and all our reviewers for their helpful comments.