The International City Image of Beijing: A Quantitative Analysis Based on Twitter Texts from 2017–2021

: With the advent of the Internet era, users from numerous countries can express their opinions on social media platforms represented by Twitter. Unearthing people’s image perceptions of cities from tweets helps relevant organizations understand the image that cities present on main-stream social media and take targeted measures to shape a good international image, which can enhance international tourists’ willingness to travel and strengthen city’s tourism competitiveness. This paper collects nearly 130,000 tweets related to “Beijing” (“Peking”) from 2017–2021 through web-crawler technology, and uses Term Frequency-Inverse Document Frequency (TF-IDF) keywords statistics, Latent Dirichlet Allocation (LDA) topic mining, and Valence Aware Dictionary and sEn-timent Reasoner (VADER) sentiment analysis to further summarize the characteristics of Beijing’s international image and propose strategies to communicate its international image. This research aims to tap into the international image of Beijing presented on Twitter, and provide data support for the relevant Chinese and Beijing authorities to develop communication strategies, as well as providing a reference for other cities aiming to manage their international image.


Introduction
With the increase in people's living standards and improvements in transportation conditions, the growing tourism consumption demand have made tourism an important point of growth and a pillar industry to drive the city economy.Therefore, research on the competitiveness of city tourism has attracted extensive attention from scholars [1][2][3][4][5].The international city image is a comprehensive reflection of its history and culture, geography and humanities, economic level and governance level.People's perception of the city image is a key factor in the choice of tourist city.Only through scientific positioning and dynamic management of its own image can a city stand out in the increasingly fierce competition in the tourist market, and thus enhance its competitiveness.By studying the perceived international image of a city, tourism enterprises and government departments can understand people's concerns and the positive and negative factors that affect people's emotions.This will help in the development of targeted communication strategies and stimulate tourism's vitality.
As the capital of China, Beijing is the political, cultural, international communication, and technology innovation center, with deep historical and cultural deposits.The success of the 2022 Beijing Winter Olympic Games made Beijing the first city to host both the Summer and Winter Olympic Games, which attracted the attention of the world and brought new opportunities to shape the Beijing's international image.By strengthening the research and management of its own international image, Beijing can give full play to its advantages, use major platforms with worldwide influence to shape a good international image, and enhance its international tourism competitiveness.

Literature Review 2.1. Definition of the International City Image
The term "city image" was first proposed by American urbanist Lynch [6] in 1964, who believed that city image is an impression shared by a certain number of city residents and includes five elements: roads, boundaries, areas, nodes and landmarks.The city image proposed by Lynch was limited to the impressions that city residents had of their places.It was restricted to the perception of the physical form of the city.Later scholars placed more emphasis on the impression of non-residential conditions [7][8][9][10][11], and incorporated city culture and spirit into the concept of "city image", i.e., city image is the public's comprehensive evaluation of the internal and external strengths and future development of a city, reflecting its characteristics and style [12,13].With the development of modern networks, some scholars have proposed that the construction of city image is a process of external communication, i.e., the city image is formed through the combination of mass media, personal experience and environmental factors [14].Further, it was suggested that the media is the main tool of city image communication, and the media encodes and decodes comprehensive information regarding the city and acts on public perceptions to eventually form the city image [15][16][17][18][19].The media has an irreplaceable role in the communication of city image due to its timeliness and comprehensiveness [20].City image is an objective social existence and a subjective social evaluation, and the construction of a city image is also the process of the city being perceived and re-evaluated by the public [21,22].According to the different subjects of city image perception, the city image can be further divided into international city image and domestic city image.The international city image is the content presented by the city image in the process of international communication, which is the city image in the eyes of the international public.The international city image relies on international communication, and good international communication ideas are conducive to the establishment of a good international city image [23][24][25][26].Therefore, understanding the current city image of Beijing on international mainstream social media is an essential part of enhancing Beijing's international image.

International City Image and City Tourism Competitiveness
In recent years, with the booming tourism industry, the relationship between the international city image and the competitiveness of urban tourism has received attention from several scholars.It has been argued that the competitiveness of a tourist destination is expressed as the perception of the tourists [27,28].Vinyals-Mirabent [29] confirmed through a study that attraction factors such as city architecture and culture are essential features of city tourism competitiveness and help to distinguish a city's image from that of its competitors.Kim and Lee [30] found that the dynamic, static, and concrete dimensions of a city's image positively influence tourists' willingness to return.Some subsequent scholars have also confirmed the view that city image is an important factor influencing the future behavioral intentions of tourists, which directly or indirectly affects their decision-making behavior [31][32][33].In addition, some studies have investigated the relationship between city tourism competitiveness and national culture and found that components of city image such as national culture have a significant impact on city tourism competitiveness [34,35].Destination image was also found to be an antecedent of destination personality, and destination personality directly affects individuals' attitudes toward visiting [36,37].Existing research on the international city image also suggests that residents' and tourists' perceptions of the city can influence the level of government support for tourism, with more positive perceptions of the city leading to more support for tourism in city-building [38][39][40].The international city image can have an impact on the competitiveness of tourism, and the international city image becomes a strategic means of development for the city to improve its tourism competitiveness.Therefore, this paper examines the city's characteristics from the perspective of Beijing's international image, and proposes image-shaping solutions to enhance Beijing's urban tourism competitiveness.

Word Separation and Word Frequency Statistics
The separation of English words was mainly performed by "word_tokenize()" in the Natural Language Toolkit (NLTK) module, a Python-based platform for a natural language processing toolset.To improve the word separation effect, this paper first preprocesses the original text, including abbreviation reduction, Uniform Resource Locator (URL) deletion, emoji deletion, "@" "#" symbol deletion, word form reduction, synonym replacement, phrase recognition and deactivation removal, to obtain the word separation results.The word frequency statistics can then be completed using "collections.Counter()".

Term Frequency-Inverse Document Frequency
The TF-IDF [41] method consists of two components: Term Frequency (TF) and Inverse Document Frequency (IDF).TF measures the frequency with which words occur in a document and can be calculated by Equation (1).
In the context of this paper, we further elaborated the meaning of the symbols represented in the formula, where i is different words, j refers to different tweets, n i,j is the number of times word i appears in tweet j, ∑ k n k,j represents the total number of times all words appear in tweet j.
IDF measures the importance of words, and its mechanism reduces the weight of common words and increases the weight of rare words.The specific calculation formula is shown in Equation (2).
where |D| is the total number of tweets, j : t i ∈ d j represents the number of tweets con- taining the word t i .If none of the tweets contain the word t i , this leads to j : To avoid the denominator of 0, we use j : Ultimately, the value of TF-IDF is obtained by multiplying the resultant value of Equation ( 1) with the resultant value of (2), as in Equation ( 3).The more important the word is, the lager its TF-IDF value will be.

Latent Dirichlet Allocation Topic Model
Word frequency statistics can simply and intuitively extract hot words, but do not consider the semantic association behind the words.Some words may be generated in the same thematic context, such as "It's very sunny outside and there is no wind" and "I ate a lot of ice cream", which both reflect the topic of "hot weather".However, if we only calculate the word frequency, each word in these two sentences is not necessarily a high-frequency word, making it difficult to uncover the topic of the text.
The LDA topic model is a text representation model that takes semantic association into account.The LDA topic model considers that an article has multiple topics and the words in the text have a certain probability of belonging to these topics.
The key to applying the LDA topic model is to determine the optimal number of topics.The effect of topic extraction in the LDA topic model is directly related to the number of potential topics, and the two most common evaluation methods to determine the number of topics are based on coherence [42][43][44] or perplexity [45].Coherence refers to the quantitative calculation of whether the semantic association of words under a topic generated by an LDA is closer, and the formula for calculating topic coherence is shown in Equation (4).
where T is a topic, v i and v j are the words within the topic, and p v i , v j is the scoring function to measure the semantic closeness of the words within the topic.
In practice, the function p v i , v j is generally used in the UMass algorithm [43], with the formula shown in Equation (5).
where p v i , v j denotes the co-occurrence probability of words v i and v j , and p(v i ) and p v j denote the occurrence probability of words v i and words v j , respectively, and ε is a smaller constant.Perplexity can be understood as how uncertain the trained model is about the topic to which article d belongs.This degree of uncertainty is perplexity, which is calculated as in Equation (6).
In the formula, M represents the number of texts in the test set, N d denotes the number of words in the dth text, and p(w d ) represents the probability of occurrence of each word in the dth text.
In summary, this paper chose the number of topics under larger coherence and smaller perplexity to complete the construction of the LDA topic model.

Valence Aware Dictionary and sEntiment Reasoner Sentiment Analysis
Sentiment analysis is a subfield of natural language processing that aims to extract attitudinal dispositions from text.The three most common methods of sentiment analysis are plain Bayesian, Long Short-Term Memory (LSTM), and VADER methods [46][47][48][49][50][51].This paper uses the powerful VADER method, which uses a comprehensive, high-quality sentiment vocabulary and complex linguistic rules to generate sentiment scores [52].In addition to the determination of sentiment words, sentiment intensity is also measured, mainly based on punctuation, degree adverbs, negation, and conjunctions.For example, in the sentence "I really can't recommend it.","recommend" is the positive sentiment word, while the previous word is the negative word, "can't", so the emotional value of "can't recommend" is −1.11 (−0.74 × 1.5).The previous word is the degree word, "really".According to the principle that the degree word that is farther away from the emotion word is assigned a lower score, the assignment of really is 0.293 × 0.95 = 0.27835.The sentiment score of the whole sentence is −0.27835 − 0.74 × 1.5 = −1.38835.A detailed introduction and code can be found at https://github.com/cjhutto/vaderSentiment(accessed on 10 July 2022).

Data Collection
This paper used Twitter (https://twitter.com/home,accessed on 5 June 2022) as the original data source for the study, and crawled tweets with "Beijing" or "Peking" between 1 January 2017 and 31 December 2021 through Python language programming.The total number of tweets with "Beijing" or "Peking" between 1 January 2017, and 31 December 2021, was 129,260, and the number of tweets per year is detailed in Table 1.

Data Processing
Firstly, this paper converts the German language in the tweets into English.Since the same two words "Beijing" and "Peking" are also used in German, the authors further used the Language Identification (LangID) module in Python to identify the German lan-guage text of each comment.Since the comment content has a small number of words and machine translation can reach a high level of accuracy, this paper used Google Translate (https://translate.google.cn,accessed on 6-7 June 2022) combined with manual proofreading and revision to complete the translation between German and English.
Subsequently, this paper used the NLTK module in Python for word separation, and the collections module in Python for word frequency (number of occurrences), and eliminated words with no real meaning based on the word frequency statistics, such as, "also", "said", "via", etc.In the case of words that are supposed to be phrases but are split into individual words, phrases are written in the dictionary, e.g., "North Korea", "Hong Kong", "New York" etc., were considered.Finally, "Beijing" and "Peking" were excluded because all tweets contained one or the other, and this was of no practical significance for the subsequent study.After pre-processing, this paper performed a second word separation, followed by TF-IDF keywords statistics, and used the LDA topic model for topic-mining and sentiment analysis.The Python code involved in this paper was written and run using PyCharm software.

Keywords Statistics
In this paper, the TF-IDF value was calculated by the TF-IDF method introduced in Section 3.2, and the keywords were ranked according to the TF-IDF value for each year, while the number of keyword occurrences was counted according to the Word Frequency Statistics method introduced in Section 3.1.
Table 2 shows the top 50 keywords of 2017."China" has the highest TF-IDF value and the most occurrences; except for the TF-IDF value of 2021, which is in second place, the TF-IDF value and occurrences were all in first place.This shows that Beijing, as the capital of China, is sometimes used as a pronoun for "China".Next, "duck" ranked second in terms of TF-IDF value and number of occurrences.Peking duck is a world-renowned Beijing dish, which has apparently become an iconic food in Beijing and even China in the eyes of friends around the world.In addition, we found many adjectives in the keywords, such as "new", "great", "good", "open", "friend".It is easy to see that the current global image of Beijing is dominated by positive adjectives, which are more related to the continuous strengthening of its international metropolis and expansions of its openness.The words "art", "glass" and "opera" were among the top 50 keywords for 2017, indicating that Beijing's traditional culture and arts are receiving attention from friends around the world.At the same time, many Twitter users expressed their desire to go to Beijing and eat Peking duck, so the keyword "want" ranked seventh in 2017.
According to the top 50 keywords of 2018 (Table 3), it is easy to see that the ranking of "trade" improved, moving from 44th place in 2017 to 7th in 2018, and "tariff", "Washington", "government", "dispute" appeared in the top 50 keywords for 2018.At this point, Beijing was more of a referent for China, and the U.S. government's imposition of tariffs on Chinese products sparked global concern."duck", "art", "opera", and "glass" remained in the top 50 keywords.The adjectives in the top 50 keywords are also all positive words.The top 50 keywords of 2019 (Table 4) feature 42 keywords from the top 50 keywords of 2018, and generally maintain the same focus as people had in 2018.
The coronavirus epidemic swept the world in 2020, and the 50 keywords of 2020 (Table 5) show that people paid more attention to the epidemic, such as "coronavirus", "virus", "corona", "pandemic", "COVID".The ranking of "duck" dropped, "art", "opera", and "glass" no longer appeared in the top 50 keywords, and the number of positive adjectives decreased.In 2021, as the epidemic improved, epidemic-related words no longer appeared in the top 50 keywords (Table 6)."duck" moved to first place, "opera" moved to fifth place, and "brooch" appeared in the top 50 keywords for the first time.Beijing will be the host city of the 24th Winter Olympic Games in 2022, and "Olympics" and "winter" were among the top 10 keywords.At the same time, more positive words, such as "love" and "friend", appeared in the top 50 keywords.This paper counted the occurrence of words that appeared in the top 50 keywords in the five-year period from 2017 to 2021, forming Figure 1, with 13 words in total.Among them, "China" and "Chinese" achieved a significant decrease in 2021, despite their increasing number in 2017-2020, which could indicate that Beijing has improved its national image and is more representative of its own city image."duck" has become a famous dish in Beijing and is known worldwide.This was especially noticeable in 2021, when the epidemic improved, and the number of occurrences of the word "duck" significantly increased."want" expresses some of the thoughts and aspirations of the global friends regarding Beijing, and "first" was more frequently used in tweets regarding Beijing.The three high-frequency adjectives, "new", "good" and "great", which were all found over the five-year period, show the positive image of Beijing.In 2021, the occurrence of the words "good" and "great" increased, but use of the word "new" decreased compared to the previous year.As Beijing has many famous universities, the word "university" is widely mentioned in tweets.By checking the original tweets, the authors found that many tweeters are studying at universities in Beijing, or hope to come to study at universities in Beijing, and some tweeters have left footprints in university libraries.
words "good" and "great" increased, but use of the word "new" decreased compared to the previous year.As Beijing has many famous universities, the word "university" is widely mentioned in tweets.By checking the original tweets, the authors found that many tweeters are studying at universities in Beijing, or hope to come to study at universities in Beijing, and some tweeters have left footprints in university libraries.

Topic Mining
Appendix A shows the coherence and perplexity of the LDA topic model for a different number of topics each year from 2017 to 2021, and this paper determined the number of topics for each year based on a larger coherence and smaller perplexity, and finally determined the number of topics for 2017-2021 as 3, 3, 4, 5, and 3, respectively, and Tables 7-11 shows the results of the LDA topic model for 2017-2021.In the results presented in 2017 (Table 7), Topic1 is Beijing school culture, which includes the words "university", "school" and some words describing school or school life.Topic2 is Beijing traditional culture, which includes Beijing traditional food "duck", as well as "art", "glass", Topic3 is closely related cities, and the cities closely related to Beijing are "New York", "Barcelona ", "Moscow", "London", "Shanghai", and many of the tweeters also traveled between these cities and Beijing.
In the results presented in 2018 (Table 8), Topic1 shows the characteristics of the combination of Topic1 and Topic2 themes in 2017, mainly consisting of "duck", "art", "opera" and traditional culture and art, as well as "university" and "student" of campus culture, which can be said to be a wide range of Beijing cultural topics.In 2018, Topic2 is the trade friction, including "trade", "Trump", "tariff", "Washington".Topic3 presents the tourism, including some cities with close ties to Beijing and the words "museum", "airport ", "flight", "car" and other tourist attractions and travel tools.In the results presented in 2019 (Table 9), Topic1 contains words such as "event", "game", "winter" and other words closely related to winter sports.After reviewing the content of the original tweets, these words were found more often found in tweets expressing expectations for the Beijing Winter Olympics.Topic2 is the campus culture, including "university", "school" and "student" and "student".Topic3 presents the same topic as Topic2 in 2018: the trade friction.Topic4 is mainly composed of the words "duck ", "glass", "food", "film", and other words that form the cultural topic.
In the results presented in 2020 (Table 10), in addition to some words representing trade friction, words such as "Biden" and "election" also appear in Topic1, so it can be said that Topic1 focuses more on the Sino-US relations.Topic2 has words such as "duck" and "restaurant", making the topic more focused on the food culture.Topic3 focuses on the epidemic, including the words "coronavirus", "virus", "corona", "outbreak ", "Wuhan", "pandemic" and other related words.Topic4 focuses on the violent impact of Hong Kong society, mainly involving "law ", "security", "HK" and other words.Topic5 is Beijing's traditional culture and sports, including "opera ", "palace", "game", "winter", "Olympics".

Sentiment Analysis
The adjectives in the keyword statistics, such as "new", "great", "good", "open", "international", and "friend", help us to understand people's emotional perception of Beijing.Most of them are adjectives that express positive emotional tendencies.
Through the VADER sentiment analysis method, this paper further quantified the sentiment values of Beijing city at various timepoints.Figure 2

Sentiment Analysis
The adjectives in the keyword statistics, such as "new", "great", "good", "open", "international", and "friend", help us to understand people's emotional perception of Beijing.Most of them are adjectives that express positive emotional tendencies.
Through the VADER sentiment analysis method, this paper further quantified the sentiment values of Beijing city at various timepoints.Figure 2    Compared to the mean value of 5-year sentiment, the majority of months in 2017, 2018, 2019 and 2021 showed sentiment values above the mean, with the exception of 2019 and 2020, where the sentiment trend line fluctuated somewhat, and 2017, 2018 and 2021 show relatively stable monthly changes.The 5-year sentiment mean line has mean values below 0.1000 in June, July and August, and above 0.1000 in all other months, and above the mean values in October and November.After further calculation, the annual sentiment values for 2017-2021 are all above 0, showing a positive sentiment tendency, with the annual sentiment values for 2017, 2018, 2019 and 2021 being above 0.1000.

Analysis of the Characteristics of the International Image of Beijing City
Beijing presents a kind of ancient capital charm.The city has a long history and is a humanistic city where cultural preservation and development go hand in hand."palace", "museum", and "wall" appear in keywords and the LDA topic model.
Secondly, tweets about Beijing reveal a certain cultural atmosphere.On the one hand, this is a traditional culture, with words such as "opera", "art", "glass", "vintage", "brooch" and so on becoming keywords.On the other hand, the campus culture, with words such as "university", "school", "student", and the many famous universities in Beijing, make the city's campus culture strong.
Third, "duck" was mentioned many times in tweets, and appeared in the top 10 keywords every year.Peking duck has become a famous Beijing specialty and a food card of Beijing.
Fourthly, Beijing, as the capital of China, has many opportunities to host major international events, which gives it a huge advantage in international communications.As the host city of the 2022 Winter Olympic Games, Beijing is highly anticipated worldwide.
Fifthly, through the adjectives included in keywords, we can describe the international image of Beijing city more clearly.Words such as "new", "great", "good", "open", "international" and "friend" show that Beijing is gradually becoming a modern and civilized international city, and that the friendliness and openness of Beijing attracts people from all over the world.
Sixthly, Beijing, as an international metropolis, has close ties with other cities, "New York", "Barcelona", "Moscow", "London", "Shanghai" appear in the high-frequency vocabulary many times, and tweeters repeatedly say that they travel between these cities and Beijing.
Finally, the paper also notes that the international city image of Beijing is vulnerable to national events, especially controversial events that can cause some people around the world to change their emotional disposition toward Beijing within a short period of time.

Strategy for Beijing International Image Communication
First, the city image is part of the national image, and the city image of Beijing is highly linked to its national and governmental image.Therefore, the communication strategy for Beijing should be an important part of a national policy, rather than a propaganda piece or a stand-alone project.The communication strategy regarding city image should focus on the creation of a clearer national consensus about Beijing's positioning, making the nation understand that a country's reputation is the property of its people, and encouraging and practicing a culture of creativity across government, culture, business, investment, education, industry, and other areas.Only by standing as a nation and encouraging creative culture, so that new ideas continue to emerge in every field, can we continue to correct and counteract existing stereotypes, reaching a situation where the image of the nation and the image of the city develop together and complement each other.
Among the adjectives in keywords, words such as "new" and "first", which are always associated with innovation, are often associated with positive content, making people feel positively about Beijing.Therefore, it is important to pay close attention to new concepts and achievements in various areas when promoting tourism in Beijing.It is also important to note that major national events that take place in Beijing will bring more direct tourism opportunities.The word "Olympics" jumped to the sixth keyword in 2021, and the Winter Olympics provided the city with the new title of the first city in the world to host both the Summer and Winter Olympic Games, bringing the new concept of "Green Olympics" and a new market of "skiing fever".Tourism organizations and practitioners should make full use of these new opportunities to innovate and promote tourism in Beijing and build a good international image of the city.
Secondly, the focus should be on promoting Beijing's image with regard to its culture, entertainment and appearance.According to data from the Beijing Municipal Cultural Heritage Bureau [53], Beijing has 138 national key cultural relics in the protection list, including the Summer Palace, the Great Wall, and the Palace Museum.It also has 144 national intangible cultural heritage representative projects [54], including the Beijing Opera, Kunqu Opera and others.There are also many modern landmarks, such as National Grand Theatre, the Water Cube and others.However, according to keywords, statistics and the results of LDA topic model, these rich and colorful cultural resources are far from being fully explored and displayed, and there is less relevant discussion.Media in China can create sub-accounts with different topic contents according to audience interests, make full use of pictures and videos to show more intuitive contents, explore the filmable resources of Beijing culture, and promote city tourism through storytelling.
Moreover, keywords statistics show that international attention to Beijing mostly occurs at the abstract level (representatives of the state and government, economic and social issues, etc.), ignoring its unique feeling as a physical space.In addition to the virtual image presented on the Internet, the physical image should be paid equal attention.The government should continue to improve and create urban humanistic landmarks, enrich the cultural and recreational life of residents, and improve transportation, roads and the ecological environment.According to the concepts of absorption and immersion in tourism management, people need to obtain immersion experiences through visual and auditory sensory perceptions, which could help them to form a deep and complex city image [55].In short, the influence of Beijing's culture and urban landscape on its overall image should be continuously expanded, which will help to create a more positive and diverse image of Beijing and allow for people to discover its diverse charms.
Finally, openness to the outside world needs to be maintained and encouraged, and the tourism organizations in Beijing should cooperate with unofficial accounts.The shaping of an international city image is a cross-regional, cross-cultural and cross-linguistic external communication.Ordinary communication only needs to convert the original information into a message that is acceptable to the general public, while external communication needs to cross multiple barriers, such as language differences and cultural differences, which means that opinion leaders play an important role in shaping the international city image.Compared with general audiences, opinion leaders are more frequently exposed to media, and they influence audiences with lower levels of media exposure, knowledge and interest by providing information and conveying their views.
With the popularity of Internet applications, the role played by online opinion-leaders is receiving more and more attention.There are two types of information sources that can play the role of opinion leaders.One is the national media of foreign publics.The other is foreign publics with direct or indirect contact with Chinese cities.During the epidemic, the international tourism market was hit hard, which also made it difficult for the foreign public to gain direct knowledge of Chinese cities.There is a Chinese idiom that originates from a historical story: "Seeing is believing".In the post-epidemic era, the government should establish a more scientific and efficient public health policy, promote international tourism recovery and development, organize study-abroad activities between universities, and establish international friendship cities, ultimately achieving image improvement and renewal.
In addition, the role of local citizens in spreading the city image should not be ignored.According to the Beijing Urban Master Plan (2016-2035) released by the Beijing government in 2017 [56], the blueprint for the future development of Beijing is to make Beijing an international, first-class, harmonious and livable capital.When residents feel that the city is livable, they will naturally be proud to promote their city to the outside world, ultimately creating an attractive urban atmosphere in Beijing.The official media and related institutions should actively cooperate with domestic and foreign unofficial media and cooperate with the agenda-setting function of traditional media, i.e., traditional media influences the focus of public attention and the perception of the social environment by giving prominence to various topics, and continuously carrying out data tracking and analysis.

Discussion
This paper searched papers with the topic "city image" through the Web of Science (https://www.webofscience.com/wos/woscc/basic-search,accessed on 12 August 2022).After browsing and sorting, we found that most studies focused on the city image in the country, but fewer studies studied the international image of cities.In addition, most studies on the international image of cities were conducted in a qualitative way, without sufficient data support.
Subsequently, this paper set the topic as "city image", "Beijing" and "Twitter", and found only six papers related to this topic.Among them, two papers used social media data to map human activities, and one paper proposed a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data.All three papers were in the field of remote sensing.In addition, one paper examined how the Burberry brand has become a major trendsetter in social media marketing.One paper examined how the Beijing Museum uses social media tools as a marketing strategy to increase its visibility among the public.Another paper proposed a fine-grained spatiotemporal dynamic pattern analysis approach and applied this to a Flickr dataset from Beijing: the paper shows that the method can also be applied to Twitter data.
In summary, international city image studies lack case studies; notably, there are few studies on the international image of Beijing.The analysis is mostly qualitative, and there are few studies on the international image of cities based on international social media platforms using text-analysis techniques.This paper further expands the research in related fields and enriches the research results.It also makes some contributions in terms of methodology, theory and practical application, including the following three aspects: 1.
From the methodological perspective, this paper further mines the international image of Beijing based on Twitter comments, using keyword extraction through the TF-IDF method, topic analysis through the LDA model and sentiment analysis through the VADER method.The combination of the three methods allowed for a more comprehensive analysis of Twitter users' perception of Beijing's city image.A social media platform text-mining framework was formed for city-image research.

2.
From the theoretical perspective, this paper extended the research related to the international image of cities and city tourism competitiveness, emphasizing that tourism competitiveness is enhanced through the city image, which is not limited to the city tourism image but includes a more comprehensive international image of cities, including economic trade, sports events, film and entertainment, and social issues.

3.
From the perspective of practical application, Beijing is currently in the promotional period of international communication center construction, and better shaping the international image of the city plays a crucial role in the future development of Beijing.
This study provides powerful data support and communication strategies to allow for relevant departments to make decisions, which, in turn, helps Beijing to establish a good international image.

Conclusions
This paper collected tweets about "Beijing" ("Peking") on Twitter from 2017 to 2021 through web-crawler technology, and analyzed the international city image presented through TF-IDF keywords statistics, LDA topic mining, and VADER sentiment analysis.

Figure 1 .
Figure 1.Trends of keywords occurring in all 5 years.

Figure 1 .
Figure 1.Trends of keywords occurring in all 5 years.
depicts the trends in sentiment value from January to December 2017-2021, and found two timepoints showed significant decreases in August 2019 and April 2020.When the image of Beijing as the capital of China was somewhat affected by the violent social shock in Hong Kong in August 2019, and the global outbreak of the coronavirus outbreak in April 2020, some global friends changed their emotional perception of China and the city of Beijing within a short period of time.The emotional perception of a city is closely related to major events.
depicts the trends in sentiment value from January to December 2017-2021, and found two timepoints showed significant decreases in August 2019 and April 2020.When the image of Beijing as the capital of China was somewhat affected by the violent social shock in Hong Kong in August 2019, and the global outbreak of the coronavirus outbreak in April 2020, some global friends changed their emotional perception of China and the city of Beijing within a short period of time.The emotional perception of a city is closely related to major events.

Figure 2 .
Figure 2. Trends in sentiment value from January to December 2017-2021.Compared to the mean value of 5-year sentiment, the majority of months in 2017, 2018, 2019 and 2021 showed sentiment values above the mean, with the exception of 2019 and 2020, where the sentiment trend line fluctuated somewhat, and 2017, 2018 and 2021 show relatively stable monthly changes.The 5-year sentiment mean line has mean values below 0.1000 in June, July and August, and above 0.1000 in all other months, and above

Figure 2 .
Figure 2. Trends in sentiment value from January to December 2017-2021.

Figure A1 .
Figure A1.(a) Coherence of the 2017 LDA Topic Model; (b) Perplexity of the 2017 LDA Topic Model; (c) Coherence of the 2018 LDA Topic Model; (d) Perplexity of the 2018 LDA Topic Model; (e) Coherence of the 2019 LDA Topic Model; (f) Perplexity of the 2019 LDA Topic Model; (g) Coherence of the 2020 LDA Topic Model; (h) Perplexity of the 2020 LDA Topic Model; (i) Coherence of the 2021 LDA Topic Model; (j) Perplexity of the 2021 LDA Topic Model.

Table 1 .
Statistics on the number of tweets from 2017 to 2021.

Table 7 .
LDA topic model results of 2017.

Table 8 .
LDA topic model results of 2018.

Table 9 .
LDA topic model results of 2019.

Table 10 .
LDA topic model results of 2020.

Table 11 .
LDA topic model results of 2021.