文章信息: 作者： kaiwu; 日期：2023年10月11日; 点击数：487

R markdown文件

https://drfs.ctcontents.com/file/20727931/833531902/c206ea/opendata/CN_bigdata.Rmd

wos引文数据

https://drfs.ctcontents.com/file/20727931/833531877/bd4a50/opendata/wos_bigdata1.bib

https://drfs.ctcontents.com/file/20727931/833531901/788007/opendata/wos_bigdata2.bib

http://kaiwu.city/index.php/citation-analysis-and-bibliometrics

1.分析前的准备工作

1.1参考资料

英文官方参考资料 https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html

R markdown文件 https://bibliometrix.org/documents/bibliometrix_Report.zip

1.2 安装、调用文献计量分析拓展功能包 bibliometrix

Install and load bibliometrix R-package

In [1]:

# # 安装方法1：通过CRAN安装稳定版
# Stable version from CRAN (Comprehensive R Archive Network)

# 如果想执行下一行代码，删除 # 即可
# install.packages("bibliometrix")


# 安装方法2：通过github安装最新版
# Most updated version from GitHub

# 如果想执行下2行代码，删除 # 即可
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
# Installation of some useful packages


# 最为稳健的方法，检测是否安装，没有安装就安装
if(!isTRUE(require("bibliometrix"))){install.packages("bibliometrix")}


# 调用bibliometrix拓展功能包
library(bibliometrix)

Loading required package: bibliometrix

Please note that our software is open source and available for use, distributed under the MIT license.
When it is used in a publication, we ask that authors properly cite the following reference:

Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, 
                        Journal of Informetrics, 11(4), pp 959-975, Elsevier.

Failure to properly cite the software is considered a violation of the license.
                        
For information and bug reports:
                        - Take a look at https://www.bibliometrix.org
                        - Send an email to info@bibliometrix.org   
                        - Write a post on https://github.com/massimoaria/bibliometrix/issues
                        
Help us to keep Bibliometrix and Biblioshiny free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)

                        
To start with the Biblioshiny app, please digit:
biblioshiny()

1.3 设定文件存储目录

In [2]:

#dataforder是文件目录，同学可以自行修改你的文件目录，注意两点
#（1）不能用汉字，必须是英文字母和数字的组合
# (2) window系统，目录用\，而这里用的跟苹果系统、linux系统一致的/，需要修改一下
# 对于一个行数很多的程序，用起来很方便。

datafolder="D:/tdata/"

1.4 文献数据检索Bibliographic Collection

web of science数据库：关于该数据库及创始人，参见http://kaiwu.city/index.php/histcite

web of science数据库官方网址（购买了授权，校园网可以访问） http://apps.webofknowledge.com/

https://www.webofscience.com/wos/woscc/summary/ca2200e5-3e90-4279-a89f-b2d2eada2656-2aab8978/relevance/1

699 results from Social Sciences Citation Index (SSCI): big data (topic) and tourism or tourist or hospitality or hotel (topic) Refined By: Document Types: Articles or Review Articles. Languages: English.

1.5导入文献计量分析数据Data Loading and Converting

first file 1-500

In [4]:

# 导入bib格式的文献数据Loading txt or bib files into R environment

# 方法1：如果已经下载了文献数据，可以使用本地文件
  D1<- paste0(datafolder,"wos_bigdata1.bib")

# 方法2：直接使用网盘分享连接，下载并导入
# 网盘链接1：国外网盘opendrive.com
# D1 <-"https://od.lk/s/172672726_f1pd3/wos_bigdata1.bib"

# 网盘链接2：国内网盘https://www.ctfile.com/
# D1 <-"https://drfs.ctcontents.com/file/20727931/557190835/131a5c/opendata/wos_bigdata1.bib"

# bib格式数据转换为R分析用数据格式
# Converting the loaded files into a R bibliographic dataframe
M1 <- convert2df(file = D1, dbsource = "isi", format = "bibtex")

Converting your isi collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!

second file 501-699

In [5]:

# Loading txt or bib files into R environment
D2 <- paste0(datafolder,"wos_bigdata2.bib")

# D2<-"https://od.lk/s/172672727_ACXwn/wos_bigdata2.bib"

# D2<-"https://drfs.ctcontents.com/file/20727931/557190837/f586ff/opendata/wos_bigdata2.bib"

# Converting the loaded files into a R bibliographic dataframe
M2 <- convert2df(file = D2, dbsource = "isi", format = "bibtex")

Converting your isi collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!

In [6]:

# 合并导入的两个文件M1和M2
#  因为列结构相同（47个变量），只是行数的合并，所以用rbind，r表示row
#  如果行数是一样的，只是增加变量，那就是列合并，cbind，c表示column
#  如果是数据库的不同表格合并，那你要用merger，join，要设置合并的主键
M699<-rbind(M1,M2)

1.6 保存数据

In [7]:

# 将处理好的数据文件保存，以便未来使用——不用每次都重新导入原始数据
# csv是用英文逗号(,)作为分隔符号的数据格式，可以直接用excel打开，几乎所有的数据分析软件都支持csv格式数据
# rda是r特有的数据格式
write.csv(M699, file = paste0(datafolder,'M699.csv'))
save(M699,file=paste0(datafolder,'M699.rda'))

#save(M699,file='D:/tdata/M699.rda')

清理缓存clear the memory

1.7 剔除两条重复记录

3.1 部分 NetMatrix699 <- biblioNetwork(M699, analysis = "co-citation", network = "references", sep = ";")

出错，错误信息如下

Warning message: "non-unique value when setting 'row.names': 'QIU Q, 2021, ISPRS INT J GEO-INF'" Error in .rowNamesDF<-(x, value = value): duplicate 'row.names' are not allowed Traceback:

biblioNetwork(M699tag, analysis = "co-citation", network = "sources",
. sep = ";")
cocMatrix(M, Field = "CR_SO", type = "sparse", n, sep, short = short)
row.names<-(*tmp*, value = M$SR)
row.names<-.data.frame(*tmp*, value = M$SR)
.rowNamesDF<-(x, value = value)
stop("duplicate 'row.names' are not allowed")

In [8]:

#剔除重复记录
M699 <- subset(M699,SR !='QIU Q, 2021, ISPRS INT J GEO-INF')

1.8 再次保存数据

In [9]:

# 将处理好的数据文件保存，以便未来使用——不用每次都重新导入原始数据
# csv是用英文逗号(,)作为分隔符号的数据格式，可以直接用excel打开，几乎所有的数据分析软件都支持csv格式数据
# rda是r特有的数据格式
write.csv(M699, file = paste0(datafolder,'M699.csv'))
save(M699,file=paste0(datafolder,'M699.rda'))

#save(M699,file='D:/tdata/M699.rda')

In [10]:

rm(list=ls())

2.描述性统计分析

以后的数据分析，可以直接从2.开始，需要再次设定一下

In [11]:

# 调用bibliometrix拓展功能包
library(bibliometrix)
# 设定工作文件夹
datafolder="D:/tdata/"

导入数据

In [12]:

load(file=paste0(datafolder,'M699.rda'))

#  load(file=url("https://od.lk/s/172672725_Ma5xJ/M699.rda"))

# load(file=url("https://drfs.ctcontents.com/file/20727931/557191120/aaffe1/opendata/M699.rda"))

文献计量学（bibliometrics）主要以量化科学产出和评估科研成果影响而闻名，也可用于展示研究的动态演变。通过这种方式，文献计量学旨在描述特定学科、或研究领域的结构以及它们如何随时间演变。换言之，文献计量方法有助于绘制科学图谱（science mapping），并且在综合多个研究方面意义较大。文献计量学是建立在一套统计方法之上的一个学科，可用于定量分析科学大数据及其随时间的演变并发现信息。网络结构通常用于对作者、论文/文档/文章、参考文献、关键字等之间的交互进行建模。

Bibliometrix 是一个开源软件，用于自动化数据分析和数据可视化。在 R中转换和上传文献数据后，Bibliometrix 执行描述性分析和不同的研究结构分析。描述性分析提供了一些关于年度研究发展、前“k”位高产作者、论文、国家和最相关关键词的快照。

2.1 主要的研究发现

In [13]:

#options(width=160)
results699 <- biblioAnalysis(M699)

summary(results699, k=10, pause=F, width=130)

MAIN INFORMATION ABOUT DATA

 Timespan                              2004 : 2022 
 Sources (Journals, Books, etc)        196 
 Documents                             697 
 Annual Growth Rate %                  18.11 
 Document Average Age                  3.96 
 Average citations per doc             20.26 
 Average citations per year per doc    3.63 
 References                            32900 
 
DOCUMENT TYPES                     
 article                         573 
 article; early access           42 
 article; proceedings paper      3 
 review                          75 
 review; early access            4 
 
DOCUMENT CONTENTS
 Keywords Plus (ID)                    1426 
 Author's Keywords (DE)                2490 
 
AUTHORS
 Authors                               1671 
 Author Appearances                    2280 
 Authors of single-authored docs       55 
 
AUTHORS COLLABORATION
 Single-authored docs                  64 
 Documents per Author                  0.417 
 Co-Authors per Doc                    3.27 
 International co-authorships %        37.16 
 

Annual Scientific Production

 Year    Articles
    2004        1
    2005        1
    2007        2
    2008        2
    2009        1
    2010        3
    2011        5
    2012        2
    2013        4
    2014       12
    2015       19
    2016       19
    2017       44
    2018       74
    2019      116
    2020      144
    2021      182
    2022       20

Annual Percentage Growth Rate 18.11 


Most Productive Authors

   Authors        Articles Authors        Articles Fractionalized
1      LAW R            18  MARIANI M                        6.42
2      LIU Y            14  LAW R                            4.88
3      MARIANI M        12  XU X                             4.08
4      NILASHI M        12  MARIANI MM                       4.00
5      LI H             11  OENDER I                         3.83
6      MARIANI MM       10  BORGHI M                         3.50
7      SAMAD S          10  LIU Y                            3.40
8      WANG S            9  MARINE-ROIG E                    3.33
9      ZHANG Y           9  JIA SS                           3.00
10     BORGHI M          8  PARK E                           2.92


Top manuscripts per citations

                                Paper                                    DOI  TC TCperYear  NTC
1  GRETZEL U, 2015, ELECTRON MARK              10.1007/s12525-015-0196-8     490      54.4 4.99
2  XIANG Z, 2015, INT J HOSP MANAG             10.1016/j.ijhm.2014.10.013    394      43.8 4.01
3  WOOD SA, 2013, SCI REP                      10.1038/srep02976             339      30.8 3.01
4  GUO Y, 2017, TOURISM MANAGE                 10.1016/j.tourman.2016.09.009 317      45.3 5.58
5  XIANG Z, 2017, TOURISM MANAGE               10.1016/j.tourman.2016.10.001 307      43.9 5.40
6  LI J, 2018, TOURISM MANAGE                  10.1016/j.tourman.2018.03.009 246      41.0 8.51
7  BUHALIS D, 2015, J DESTIN MARK MANAG        10.1016/j.jdmm.2015.04.001    191      21.2 1.94
8  CARLOS GARCIA-PALOMARES J, 2015, APPL GEOGR 10.1016/j.apgeog.2015.08.002  180      20.0 1.83
9  CHENG M, 2019, INT J HOSP MANAG             10.1016/j.ijhm.2018.04.004    168      33.6 6.99
10 MARINE-ROIG E, 2015, J DESTIN MARK MANAG    10.1016/j.jdmm.2015.06.004    167      18.6 1.70


Corresponding Author's Countries

          Country Articles   Freq SCP MCP MCP_Ratio
1  CHINA               197 0.2835 137  60    0.3046
2  USA                 102 0.1468  65  37    0.3627
3  UNITED KINGDOM       59 0.0849  24  35    0.5932
4  SPAIN                52 0.0748  40  12    0.2308
5  KOREA                40 0.0576  27  13    0.3250
6  ITALY                36 0.0518  26  10    0.2778
7  AUSTRALIA            22 0.0317   6  16    0.7273
8  PORTUGAL             16 0.0230  12   4    0.2500
9  INDIA                14 0.0201  13   1    0.0714
10 AUSTRIA              11 0.0158  10   1    0.0909


SCP: Single Country Publications

MCP: Multiple Country Publications


Total Citations per Country

      Country      Total Citations Average Article Citations
1  USA                        3533                     34.64
2  CHINA                      2497                     12.68
3  UNITED KINGDOM             1771                     30.02
4  KOREA                      1006                     25.15
5  SPAIN                       975                     18.75
6  ITALY                       789                     21.92
7  AUSTRALIA                   613                     27.86
8  FINLAND                     275                     68.75
9  AUSTRIA                     265                     24.09
10 GERMANY                     261                     23.73


Most Relevant Sources

                                                 Sources        Articles
1  SUSTAINABILITY                                                     65
2  TOURISM MANAGEMENT                                                 54
3  INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT       48
4  INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT                    38
5  CURRENT ISSUES IN TOURISM                                          27
6  TOURISM ECONOMICS                                                  18
7  TOURISM REVIEW                                                     18
8  JOURNAL OF DESTINATION MARKETING \\& MANAGEMENT                    17
9  JOURNAL OF TRAVEL RESEARCH                                         17
10 JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY                      16


Most Relevant Keywords

   Author Keywords (DE)      Articles Keywords-Plus (ID)     Articles
1         BIG DATA                184          BIG DATA           211
2         TOURISM                  62          TOURISM            119
3         SOCIAL MEDIA             49          HOSPITALITY        100
4         ONLINE REVIEWS           41          SOCIAL MEDIA        94
5         BIG DATA ANALYTICS       38          IMPACT              93
6         SENTIMENT ANALYSIS       35          MANAGEMENT          75
7         MACHINE LEARNING         32          MODEL               71
8         TEXT MINING              30          SATISFACTION        69
9         HOSPITALITY              26          WORD-OF-MOUTH       67
10        COVID-19                 24          ANALYTICS           56

In [14]:

plot(x=results699, k=10, pause=F)

2.2 引用最多的参考文献 Most Cited References

In [15]:

CR699 <- citations(M699, field = "article", sep = ";")

# 这里列出了被引用次数最高的，前20的参考文献
# 注意doi，有了doi，可以快速导入zotero等文献管理软件，完整的文献信息
cbind(CR699$Cited[1:20])

A matrix: 20 × 1 of type int
XIANG Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/J.IJHM.2014.10.013	123
LI JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/J.TOURMAN.2018.03.009	116
XIANG Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/J.TOURMAN.2016.10.001	70
GUO Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/J.TOURMAN.2016.09.009	67
MARIANI M, 2018, INT J CONTEMP HOSP M, V30, P3514, DOI 10.1108/IJCHM-07-2017-0461	54
LIU Y, 2017, TOURISM MANAGE, V59, P554, DOI 10.1016/J.TOURMAN.2016.08.012	52
XIANG Z, 2010, TOURISM MANAGE, V31, P179, DOI 10.1016/J.TOURMAN.2009.02.016	52
FUCHS M, 2014, J DESTIN MARK MANAGE, V3, P198, DOI 10.1016/J.JDMM.2014.08.002	50
MIAH SJ, 2017, INFORM MANAGE-AMSTER, V54, P771, DOI 10.1016/J.IM.2016.11.011	47
VU HQ, 2015, TOURISM MANAGE, V46, P222, DOI 10.1016/J.TOURMAN.2014.07.003	47
LEUNG D, 2013, J TRAVEL TOUR MARK, V30, P3, DOI 10.1080/10548408.2013.750919	45
YANG X, 2015, TOURISM MANAGE, V46, P386, DOI 10.1016/J.TOURMAN.2014.07.019	44
ZHAO YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/J.IJHM.2018.03.017	44
BUHALIS D, 2008, TOURISM MANAGE, V29, P609, DOI 10.1016/J.TOURMAN.2008.01.005	42
LI X, 2017, TOURISM MANAGE, V59, P57, DOI 10.1016/J.TOURMAN.2016.07.005	42
XU X, 2016, INT J HOSP MANAG, V55, P57, DOI 10.1016/J.IJHM.2016.03.003	42
YE Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/J.IJHM.2008.06.011	42
BANGWAYO-SKEETE PF, 2015, TOURISM MANAGE, V46, P454, DOI 10.1016/J.TOURMAN.2014.07.014	39
CANTALLOPS AS, 2014, INT J HOSP MANAG, V36, P41, DOI 10.1016/J.IJHM.2013.08.007	39
CHUA A, 2016, TOURISM MANAGE, V57, P295, DOI 10.1016/J.TOURMAN.2016.06.013	39

3. 共被引用分析 Co-citation Analysis

引文分析（Citation analysis）是文献计量学（bibliometrics）的经典技术之一。它通过节点（例如作者、论文、期刊）之间的联系显示特定领域的结构，而边缘可以根据网络类型进行不同的解释，即共同被引、直接引用、书目耦合。请参阅 Aria，Cuccurullo (2017)。 Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

下面是三个例子。

首先，被引参考文献（节点）之间关系的共引网络（co-citation networks）。

其次，使用被引期刊作为分析单位的共引网络。

评论共引网络的有用维度是：（i）节点的中心性和外围性 centrality and peripherality of nodes，（ii）它们的接近度和距离their proximity and distance，（iii）联系强度strength of ties，（iv）聚类clusters，（iiv）展示研究贡献之间的关系bridging contributions。

第三，引文编年图（historiograph）是建立在直接引用的基础上的，它按照历史顺序绘制了研究成果间的联系，足以重建该领域的发展历程，呈现该领域的里程碑式的重要研究。

3.1 被引用文章共被引分析 Article (References) co-citation analysis

Plot options:

n = 50 (the funxtion plots the main 50 cited references)
type = "fruchterman" (the network layout is generated using the Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 0.7 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
edges.min = 5 (plots only edges with a strength greater than or equal to 5)
all other arguments assume the default values

In [16]:

# 这个部分需要57.93045秒
# 下面两行注释语句，用于保存图片为png格式文件，感兴趣同学自己研究一下
NetMatrix699 <- biblioNetwork(M699, analysis = "co-citation", network = "references", sep = ";")

In [17]:

#png(paste(datafolder,'net699n50.png',sep=""),width = 1024, height = 1024,units = "px",pointsize = 12, bg = "white", res = NA, restoreConsole = TRUE)

net699n50=networkPlot(NetMatrix699, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)

#dev.off()

Descriptive analysis of Article co-citation network characteristics

In [11]:

#netstat699 <- networkStat(NetMatrix699)

#summary(netstat699,k=10)
# 这个部分太慢，我注释掉了

3.2 期刊共被引分析 Journal (Source) co-citation analysis

In [18]:

M699tag=metaTagExtraction(M699,"CR_SO",sep=";")

NetMatrix699tag <- biblioNetwork(M699tag, analysis = "co-citation", network = "sources", sep = ";")

net699n50tag=networkPlot(NetMatrix699tag, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)

期刊共被引分析结果的描述性统计分析

Descriptive analysis of Journal co-citation network characteristics

In [19]:

netstat699tag <- networkStat(NetMatrix699tag)

summary(netstat699tag,k=10)

Main statistics about the network

 Size                                  11891 
 Density                               0.01 
 Transitivity                          0.135 
 Diameter                              4 
 Degree Centralization                 0.773 
 Average path length                   2.147

4.引文编年图Historiograph

4.1 构建编年图网络

In [20]:

# 下面两行注释语句，用于保存图片为png格式文件，感兴趣同学自己研究一下

histresults699 <- histNetwork(M699, min.citations=quantile(M699$TC,0.75), sep = ";")

WOS DB:
Searching local citations (LCS) by reference items (SR) and DOIs...

Analyzing 47541 reference items...

Found 174 documents with no empty Local Citations (LCS)

4.2 编年图网络可视化

In [21]:

#options(width = 1024)
#png(paste(datafolder,'histresults699.png',sep=""),width = 1200, height = 800,units = "px",pointsize = 15, bg = "white", res = NA, restoreConsole = TRUE)

#net699hist <- histPlot(histresults699, n=20, size = 5, labelsize = 5)

#dev.off()

#net699hist

In [22]:

histresults699 <- histNetwork(M699, min.citations=quantile(M699$TC,0.75), sep = ";")

net699hist <- histPlot(histresults699, n=20, size = 5, labelsize = 3)

WOS DB:
Searching local citations (LCS) by reference items (SR) and DOIs...

Analyzing 47541 reference items...

Found 174 documents with no empty Local Citations (LCS)

 Legend

                                                                        Label
1                     YANG Y, 2014, J TRAVEL RES DOI 10.1177/0047287513500391
2              XIANG Z, 2015, INT J HOSP MANAG DOI 10.1016/J.IJHM.2014.10.013
3                GRETZEL U, 2015, ELECTRON MARK DOI 10.1007/S12525-015-0196-8
4          SCHUCKERT M, 2015, INT J HOSP MANAG DOI 10.1016/J.IJHM.2014.12.007
5          PHILANDER K, 2016, INT J HOSP MANAG DOI 10.1016/J.IJHM.2016.02.001
6          MARIANI MM, 2016, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2015.12.008
7                      PAN B, 2017, J TRAVEL RES DOI 10.1177/0047287516669050
8             XIANG Z, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.10.001
9                LI X, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.07.005
10              LIU Y, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.08.012
11              GUO Y, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.09.009
12 TALON-BALLESTERO P, 2018, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2018.03.017
13         MARIANI MM, 2018, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2017.11.006
14                  ALAEI AR, 2019, J TRAVEL RES DOI 10.1177/0047287517747753
15               MARIANI M, 2019, IEEE ACCESS DOI 10.1109/ACCESS.2018.2887300
16             AHANI A, 2019, INT J HOSP MANAG DOI 10.1016/J.IJHM.2019.01.003
17                      MARIANI M, 2020, TOUR REV DOI 10.1108/TR-06-2019-0259
                                                                                                                   Author_Keywords
1                                TOURISM DEMAND FORECASTING; TIME SERIES; ONLINE DATA; HOTEL OCCUPANCY;; BIG DATA; WEBSITE TRAFFIC
2                                                      BIG DATA; TEXT ANALYTICS; GUEST EXPERIENCE; SATISFACTION; HOTEL; MANAGEMENT
3      SMART TOURISM; SMART TECHNOLOGY; SMART BUSINESS ECOSYSTEMS; BUSINESS; MODELS; OPEN INNOVATION; BIG DATA; INTERNET OF THINGS
4                                            ONLINE RATINGS; TRAVELER DISTRIBUTION; REPUTATION; SATISFACTION; DIFFERENCE; LANGUAGE
5                                                                                                                             <NA>
6      SOCIAL MEDIA; FACEBOOK; BIG DATA; DESTINATION MANAGEMENT ORGANIZATIONS;; ENGAGEMENT; DESTINATION MARKETING; ITALIAN REGIONS
7  TOURISM DEMAND FORECASTING; BIG DATA; WEB TRAFFIC; TIME SERIES; MARKOV; SWITCHING DYNAMIC REGRESSION MODEL; SEARCH ENGINE QUERY
8                   ONLINE REVIEWS; HOTEL INDUSTRY; INFORMATION QUALITY; SOCIAL MEDIA; ANALYTICS; TEXT ANALYTICS; MACHINE LEARNING
9        TOURISM DEMAND FORECAST; BIG DATA ANALYTICS; SEARCH QUERY DATA;; GENERALIZED DYNAMIC FACTOR MODEL; COMPOSITE SEARCH INDEX
10                                              BIG DATA; SATISFACTION; HOTEL; ONLINE REVIEWS; USER-GENERATED REVIEW;; TRIPADVISOR
11                               ONLINE REVIEWS; VISITOR SATISFACTION; DATA MINING; LATENT DIRICHLET; ANALYSIS; PERCEPTUAL MAPPING
12           BIG DATA; HOSPITALITY INDUSTRY; CUSTOMER RELATIONSHIP MANAGEMENT; CLIENT; PROFILE; BOOTSTRAP RESAMPLING; HOTEL CHAINS
13                                             BOOKING.COM; ONLINE REVIEWS; LONDON BIG DATA; HOTEL RATINGS; HOTEL; CLASS; SKEWNESS
14                                                           BIG DATA; SENTIMENT ANALYSIS; SOCIAL MEDIA; LEXICON; MACHINE LEARNING
15                                              BIG DATA; ONLINE REVIEWS; ONLINE RATING; EXPEDIA.COM; HOTEL; CULTURAL; DIFFERENCES
16           SPA HOTELS; MARKET SEGMENTATION; TRAVELLER PREFERENCE; ONLINE REVIEW;; SOCIAL BIG DATA; TRIPADVISOR; MACHINE LEARNING
17                                                            BIG DATA; TOURISM; HOSPITALITY; BIG DATA ANALYTICS; PLATINUM JUBILEE
                                                                                                                                                                                          KeywordsPlus
1                                                                                                                                                                                        MODELS; TESTS
2                                                                                                                                                      ONLINE REVIEWS; INFORMATION; SELECTION; QUALITY
3                                                                                                INTERNET; CITIES; INFORMATION; TECHNOLOGY; MANAGEMENT; INNOVATION;; NETWORKS; PROGRESS; THINGS; CHINA
4                                                                                                                   NATIONAL CULTURE; SERVICE QUALITY; SATISFACTION; PERCEPTIONS;; TRIPADVISOR; IMPACT
5                                                            USER-GENERATED CONTENT; WORD-OF-MOUTH; ONLINE HOTEL REVIEWS; SOCIAL; MEDIA; BIG DATA; TOURISM; CONSUMERS; HOSPITALITY; IMPACT; INTENTIONS
6                                                                                                              SOCIAL MEDIA; INFORMATION-SOURCES; ONLINE TRAVEL; TOURISM; TECHNOLOGY;; IMAGE; INTERNET
7                                                                                                                             TOURISM DEMAND; TIME-SERIES; ARRIVALS; DETERMINANTS; INDUSTRY; CYCLE; UK
8                                                                                CUSTOMER REVIEWS; BIG DATA; SATISFACTION; TRIPADVISOR; PERFORMANCE;; READABILITY; EXPERIENCE; WEBSITE; HOTELS; SEARCH
9                                                                                                          DYNAMIC-FACTOR MODEL; BIG DATA; NEURAL-NETWORK; ARRIVALS; ANALYTICS;; ACCURACY; REVIEWS; US
10                                                                                                                                 WORD-OF-MOUTH; SERVICE QUALITY; PERCEIVED VALUE; BEHAVIOR; ADOPTION
11                                                                 WORD-OF-MOUTH; CUSTOMER SATISFACTION; SERVICE QUALITY; HOTEL REVIEWS;; USER REVIEWS; PRODUCT; PERCEPTIONS; IMPACT; SALES; CONSUMERS
12                                                REPEAT VISITORS; TOURIST MOTIVATIONS; REVENUE MANAGEMENT; DESTINATION; IMAGE; HOSPITALITY; 1ST-TIME; CRM; IMPLEMENTATION; SEGMENTATION;; PERFORMANCE
13                                                                                    WORD-OF-MOUTH; SCORING SYSTEM; ONLINE; REVIEWS; SATISFACTION;; EXPERIENCE; PLATFORMS; ANALYTICS; TOURISM; SEARCH
14                                                                                                                      ONLINE REVIEWS; SOCIAL MEDIA; TRAVEL; CLASSIFICATION; DESTINATIONS;; ANALYTICS
15                                                                                WORD-OF-MOUTH; QUALITY PERCEPTIONS; HOSPITALITY; CHALLENGES; PLATFORMS;; ANALYTICS; BEHAVIOR; DRIVERS; SCIENCE; EWOM
16 ARTIFICIAL NEURAL-NETWORKS; BIG DATA ANALYTICS; K-MEANS ALGORITHM;; QUALITY-OF-LIFE; SOCIAL MEDIA; CUSTOMER SATISFACTION; RECOMMENDER; SYSTEM; DESTINATION IMAGE; CONSUMER REVIEWS; SERVICE QUALITY
17                                                                                                                                               CUSTOMER SATISFACTION; DESTINATION; SERVICES; LESSONS
                             DOI Year LCS GCS
1       10.1177/0047287513500391 2014  38 131
2     10.1016/j.ijhm.2014.10.013 2015 123 394
3      10.1007/s12525-015-0196-8 2015  31 490
4     10.1016/j.ijhm.2014.12.007 2015  25  81
5     10.1016/j.ijhm.2016.02.001 2016  19  82
6  10.1016/j.tourman.2015.12.008 2016  21 153
7       10.1177/0047287516669050 2017  26  84
8  10.1016/j.tourman.2016.10.001 2017  70 307
9  10.1016/j.tourman.2016.07.005 2017  42 153
10 10.1016/j.tourman.2016.08.012 2017  52 117
11 10.1016/j.tourman.2016.09.009 2017  67 317
12 10.1016/j.tourman.2018.03.017 2018  18  49
13 10.1016/j.tourman.2017.11.006 2018  22  64
14      10.1177/0047287517747753 2019  38 150
15   10.1109/ACCESS.2018.2887300 2019  17  31
16    10.1016/j.ijhm.2019.01.003 2019  21  63
17       10.1108/TR-06-2019-0259 2020  17  41

5. 概念图结构：共词分析The conceptual structure - Co-Word Analysis

共词网络（Co-word networks）显示概念结构，通过术语共现（term co-occurences）揭示概念之间的联系。概念结构通常用于理解学者所涵盖的主题（研究前沿research front），并确定哪些是最重要和最近的问题。将整个时间跨度划分为不同的时间片（timeslices），并比较概念结构，有助于分析主题随时间的演变。

Bibliometrix 能够分析关键词，也可以分析文章标题和摘要中的术语。它使用网络分析或对应分析（correspondance analysis，CA）或多重对应分析（multiple correspondance analysis，MCA）来完成。 CA 和 MCA 在二维坐标系中可视化概念结构。

5.1 共词分析：基于关键词的共同出现

Co-word Analysis through Keyword co-occurrences