- 文章信息
- 作者： kaiwu
- 点击数：17
1.word操作
http://kaiwu.city/openfiles/academic_report_SPSS_CN.docx
http://kaiwu.city/openfiles/example_thesis_CN2023.doc
2.词云图
http://kaiwu.city/index.php/tag-cloud
https://www.jasondavies.com/wordcloud/
https://www.yelp.com/biz/home-coffee-roasters-san-francisco-7?osq=CAFE
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Affogatos are probably one of my fave treats. During my recent visit, decided to try something different. There's the affogato and then there's Home's Cookie Monster-gato which sounded very interesting. Essentially it's the affogato with cookie butter and little cookie cereal. Barista stated that the cookie butter makes the espresso creamier. Decided to try it. OMG!!! Sooo good! Definitely creamier and such a decadent treat. Daughter got one too. She offered some to her dad who initially declined but then took one taste and finished it off. Needed to order another one. As I write this, I'm already craving another one.
I would say this place is averagely good. Obviously the drinks are decorated very well but I wouldn't say the flavors are anything special. I was excited to try this place based on the reviews and photos but I have to say I was sort of let down. Nevertheless, the drinks didn't taste bad and I got the fix of coffee that I needed. We ordered the Birthday Cake latte (hot) and Cookie Monster latte (iced). Both were ok, the Cookie Monster had a little bit more flavor because of the cookie butter at the bottom, but still I feel this place is mainly focused on making the drinks looks pretty, and they are definitely succeeding in that respect. Overall, I don't think I will return but if you are looking for pretty lattes to post on social media this is definitely the place for you!
Updated review for Oct 2021. No indoor seating or public bathrooms available. To-go ordering only. Coffee quality is still great, but sad indoor dining still wasn't available despite many other areas re-opening up partially/completely. If one is looking for indoor cafe seating, walk a few blocks down to Henry's coffee house where indoor/outdoor patio is available.
Excellent iced salted caramel latte and Bravo egg avocado toast!! Excellent workers and food. Happy I tried this coffee spot today!! |
https://spectrum.ieee.org/top-programming-languages-2022
|
100 |
python |
|
97 |
C |
|
89 |
C++ |
|
87 |
C# |
|
70 |
Java |
|
47 |
SQL |
|
40 |
JavaScript |
|
19 |
R |
|
18 |
HTML |
|
17 |
TypeScript |
|
13 |
Go |
|
13 |
PHP |
|
10 |
Shell |
|
9 |
Ruby |
|
9 |
Scala |
|
8 |
Matlab |
|
7 |
SAS |
https://zhuanlan.zhihu.com/p/90665345
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无意中发现的店，一直躺在收藏夹里，工作日的下午跟朋友特意过去打卡，下雨天，人还是不少，室内室外都能坐，先找位置，然后桌上二维码扫码点单的！我们先逛了一圈，很多小姐姐都在拍照，还有个草坪，天气好的时候拍照会很出片！南瓜汤：味道浓郁，略浓稠的汤，上面撒的杏仁粒很香很脆！下次准备再去，吃点别的！
风和日丽的日子，总是热闹的，野餐、遛狗，喝咖啡~~ 一尺花园，古色古香的中式老房子与西式美食、咖啡结合别有特色，周末人太多了有点遗憾。在这里不迷恋他家的咖啡，居然是别人家的狗狗，萌宠治愈心情，阳光下草地上都好可爱哦。
夏天的时候一家子到川沙玩，打卡了川沙网红一尺花园。一进大门就小池鲤鱼，再往里走，大厅有好几个艺术雕像。 我们是下午到的，先点了茶饮料咖啡·蛋糕乡，都挺好吃，但是大厅太热，空调不足，我一直汗哒哒滴。 外面有很大草坪花园，很多人带着孩子或者毛孩子在外面奔跑，我害怕太阳，没有出去逛。 我们坐到晚上，服务员给我换了一间房，空调足了，点了个套餐，解决了晚餐。 总体环境意境不错，适合拍照打卡，晚餐不如下午茶，东西太咸。
马上要降温了，又不能出沪，赶着最后的暖冬带孩子出来玩 这家一尺花园陶家宅亻号店就在我们住的酒店隔壁，因为本身就是一个百年宅邸，也是酒店的宣传卖点之一，预定酒店赠送 了两张咖啡券，于是就想着不行到这边喝杯咖啡。 环境确实不错，门口有个草坪草坪里还有个沙坑很受小朋友的欢迎，至于滑滑梯等玩乐设施早就封底熬了，上面用绳子拦 着写着设施故障啥的，反正就是给人年久失修没人维护的破落感。一开始进去点咖啡，服务员看我拿的是酒店赠送的券，很有点不耐烦的感觉，咖啡甜品单价都挺高，这个新品杏仁慕斯我觉得甜了点，后来家里人来了想想也懒得开车出去，就干脆在这里点了中午正餐，味道还不错，牛肉不老当然价格也是很高，面包酥脆挺入味，不过总的来说性价比一般。户外座位确实挺紧俏，几乎坐满，想想如果不是住店，专门跑那么远来喝杯咖啡，我也是挺佩服的，换我是不太会。
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3.chatGPT and DALLE
http://kaiwu.city/index.php/sscijournal
https://www.tandfonline.com/doi/full/10.1080/14616688.2020.1750683
Sci-hub
https://sci-hub.se/
https://sci-hub.st/
https://sci-hub.ru/
1.示例本科毕业论文模板
- 文章信息
- 作者： kaiwu
- 点击数：25
https://iconbooks.com/group/30-second/
30-SECOND
Books in the bestselling 30-Second series each open up a key area in the arts and sciences, with each concept within designed to be understood in just half a minute. From politics to maths, quantum theory to the brain.
- 文章信息
- 作者： kaiwu
- 点击数：25
https://www.introducingbooks.com/
https://en.wikipedia.org/wiki/Introducing..._(book_series)
Introducing...
The Introducing... series is a book series of graphic guides covering key thinkers and topics in philosophy, psychology and science, and many others in politics, religion, cultural studies, linguistics and other areas. Books are written by an expert in the field and illustrated, comic-book style, by a leading graphic artist.
Introducing... series是一系列图解指南，涵盖哲学、心理学和科学以及政治、宗教、文化研究、语言学和其他领域的许多其他思想家和主题。 书籍由该领域的专家撰写，并由一位领先的图形艺术家以漫画风格绘制插图
中国有翻译的版本：视读书系
https://book.douban.com/series/5182
- 文章信息
- 作者： kaiwu
- 点击数：24
https://www.forbeginnersbooks.com/
https://en.wikipedia.org/wiki/For_Beginners
The For Beginners series was launched in the mid-1970s, but became out of print and often unavailable after the 2001 death of co-founder and publisher Glenn Thompson. In 2007, a consortium of investors revived the series, reprinted back issues, and promised to publish between six and nine new issues each year. The current publisher is Dawn Reshen-Doty.
- 文章信息
- 作者： kaiwu
- 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
# # 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
#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/
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
# 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
# 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!
# 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
# 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")
#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
# 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')
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
# 调用bibliometrix拓展功能包
library(bibliometrix)
# 设定工作文件夹
datafolder="D:/tdata/"
导入数据
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
#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
plot(x=results699, k=10, pause=F)
2.2 引用最多的参考文献 Most Cited References
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])
| 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
# 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 = ";")
#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
#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
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
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
# 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
#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
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
Plot options:
-
normalize = "association" (the vertex similarities are normalized using association strength)
-
n = 50 (the function 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 the vertices)
-
remove.multiple=FALSE (multiple edges are not removed)
-
labelsize = 3 (defines the max size of vertex labels)
-
label.cex = TRUE (The vertex label sizes are proportional to their degree)
-
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
-
label.n = 30 (Labels are plotted only for the main 30 vertices)
-
edges.min = 25 (plots only edges with a strength greater than or equal to 2)
-
all other arguments assume the default values
NetMatrix699keyword <- biblioNetwork(M699, analysis = "co-occurrences", network = "keywords", sep = ";")
net699keyword=networkPlot(NetMatrix699keyword, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=3,label.cex=TRUE,label.n=30,edges.min=2)
共词网络特征的描述性分析 Descriptive analysis of keyword co-occurrences network characteristics
netstat699keyword <- networkStat(NetMatrix699keyword)
summary(netstat699keyword,k=10)
Main statistics about the network Size 1426 Density 0.014 Transitivity 0.174 Diameter 5 Degree Centralization 0.42 Average path length 2.527
5.2 基于对应分析的共词分析
Co-word Analysis through Correspondence Analysis
CS699 <- conceptualStructure(</