- 文章信息
- 作者: kaiwu
- 点击数:274
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:198
1. Install python and IDE for python
1.1 install python
http://kaiwu.city/index.php/python3
https://www.python.org/downloads/
1.2 IDE(Integrated Development Environment) for python
https://hackr.io/blog/best-python-ide
An integrated development environment (IDE) is a software application that helps programmers to develop software efficiently. It's where you build your Python projects!
It increases developer productivity by combining common developer tools such as software editing, building, testing, debugging, and packaging in one easy-to-use graphical user interface (GUI).
Other popular features include code refactoring, code search, code auto-completion, and continuous integration/continuous deployment (CI/CD).
Regardless of your preferred programming language or type of software development, an IDE will be one of your go-to tools.
Moving on to the IDE's cousin, the code editor.
Sometimes mistaken for IDEs, the main difference is that IDEs provide more powerful tools to simplify the coding process.
JupyterLab
Install JupyterLab with pip
:
pip install jupyterlab
Note: If you install JupyterLab with conda or mamba, we recommend using the conda-forge channel.
Once installed, launch JupyterLab with:
jupyter lab
Jupyter Notebook
Install the classic Jupyter Notebook with:
To run the notebook:
2.An introduction to python
2.1 leap year
year = int(input("please type in a four digit year value ")); if year % 4 == 0 and year % 100 != 0: print(year, "is a leap year") elif year % 100 == 0: print(year, "is not a leap year") elif year % 400 ==0: print(year, " is a leap year ") else: print(year, " is not a leap year ") |
2.2 take control of excel
the hotel reservation dataset is downloaded from kaggle
https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset
we use a subset (199 records)of the hotel reservation dataset (36275 records).
cmd
install openpyxl library
pip install openpyxl
http://kaiwu.city/openfiles/Hotel_Reservations199.xlsx
import openpyxl as xl; filename ="D:/tdata/hotel_reservation/Hotel_Reservations199.xlsx" print(mr) from openpyxl import Workbook # create a new work # i is the row index # save the excel file |
http://kaiwu.city/openfiles/python_loop_excel.ipynb
choose the weblink,
save link as..
3.sentiment analysis
3.1jupyter notebook
icon_ipynb.png
http://kaiwu.city/openfiles/EN_sentiment_analysis_Disneyland_tripadvisor.ipynb
- Select the weblink
- right-click the mouse,choose 'Save As'
- download EN_sentiment_analysis_Disneyland_tripadvisor.ipynb
3.2dataset
http://kaiwu.city/openfiles/DisneylandReviews.csv
https://github.com/DataScience-in-Tourism
The sample dataset for this chapter includes a few rows. so in this file, the sample dataset has been replaced with the dataset from Disneyland on Kaggle (TripAdvisor). The codes has been adjusted accordingly, correcting a few mistakes.
download weblink
https://www.kaggle.com/datasets/arushchillar/disneyland-reviews
About Dataset
The dataset includes 42,000 reviews of 3 Disneyland branches - Paris, California and Hong Kong, posted by visitors on Trip Advisor.
Column Description:
Review_ID: unique id given to each review
Rating: ranging from 1 (unsatisfied) to 5 (satisfied)
Year_Month: when the reviewer visited the theme park
Reviewer_Location: country of origin of visitor
Review_Text: comments made by visitor
Disneyland_Branch: location of Disneyland Park
3.3datasets after data clean
Hong Kong Disneyland were with 786 reviews in 2019
http://kaiwu.city/openfiles/sentiment_hk_disneyland2019.csv
Hong Kong Disneyland were with 211 reviews in January 2019
http://kaiwu.city/openfiles/sentiment_hk_disneyland2019Jan.csv
3.4book chapter
http://kaiwu.city/openfiles/sentiment_analysis_Applied Data Science in Tourism - Roman Egger.pdf
参考资料:
Kirilenko, A. P., Wang, L., & Stepchenkova, S. O. (2022). Sentiment Analysis: Gaging Opinions of Large Groups. In R. Egger, Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications (pp. 363–374). Springer International Publishing. https://doi.org/10.1007/978-3-030-88389-8_17
(1)springer
https://doi.org/10.1007/978-3-030-88389-8
(2)book site
http://www.datascience-in-tourism.com/
(3)github
https://github.com/DataScience-in-Tourism
- 文章信息
- 作者: kaiwu
- 点击数:378
Bibliographic Collection
963 results from Social Sciences Citation Index (SSCI):
big data (All Fields) and tourism or tourist or hospitality or hotel (All Fields)
Refined By:
Document Types: Articles or Review Articles.
Languages: English.
big data and tourism, hospitality: A bibliometric analysis
kai Wu
Mar 18, 2022
- Bibliographic Collection
- Install and load bibliometrix R-package
- Data Loading and Converting
- Section 1: Descriptive Analysis
- Section 2: The Intellectual Structure of the field - Co-citation Analysis
- Section 3: Historiograph - Direct citation linkages
- Section 4: The conceptual structure - Co-Word Analysis
- Section 5: Thematic Map
- Section 6: The social structure - Collaboration Analysis
Bibliographic Collection
963 results from Social Sciences Citation Index (SSCI):
big data (All Fields) and tourism or tourist or hospitality or hotel (All Fields) Refined By: Document Types: Articles or Review Articles. Languages: English.
Install and load bibliometrix R-package
# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line
# install.packages("bibliometrix")
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
# Installation of some useful packages
if(!isTRUE(require("bibliometrix"))){install.packages("bibliometrix")}
## 载入需要的程辑包:bibliometrix
## To cite bibliometrix in publications, please use:
##
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis,
## Journal of Informetrics, 11(4), pp 959-975, Elsevier.
##
##
## https://www.bibliometrix.org
##
##
## For information and bug reports:
## - Send an email to info@bibliometrix.org
## - Write a post on https://github.com/massimoaria/bibliometrix/issues
##
## Help us to keep Bibliometrix 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 shiny web-interface, please digit:
## biblioshiny()
library(bibliometrix)
Data Loading and Converting
datafolder="D:/datasets/citations_data/bigdata2022record699/"
first file 1-500
# Loading txt or bib files into R environment
#D1 <- paste0(datafolder,"wos_bigdata1.bib")
D1 <-"https://od.lk/s/172672726_f1pd3/wos_bigdata1.bib"
# 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<-"https://od.lk/s/172672727_ACXwn/wos_bigdata2.bib"
#D2 <- paste0(datafolder,"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!
M699<-rbind(M1,M2)
#write.csv(M1, file = paste0(datafolder,'M1.csv'))
#write.csv(M2, file = paste0(datafolder,'M2.csv'))
save(M699,file=paste0(datafolder,'M699.rda'))
Section 1: Descriptive Analysis
load(file=url("https://od.lk/s/172672725_Ma5xJ/M699.rda"))
#load(file=paste0(datafolder,'M699.rda'))
Although bibliometrics is mainly known for quantifying the scientific production and measuring its quality and impact, it is also useful for displaying and analysing the intellectual, conceptual and social structures of research as well as their evolution and dynamical aspects.
In this way, bibliometrics aims to describe how specific disciplines, scientific domains, or research fields are structured and how they evolve over time. In other words, bibliometric methods help to map the science (so-called science mapping) and are very useful in the case of research synthesis, especially for the systematic ones.
Bibliometrics is an academic science founded on a set of statistical methods, which can be used to analyze scientific big data quantitatively and their evolution over time and discover information. Network structure is often used to model the interaction among authors, papers/documents/articles, references, keywords, etc.
Bibliometrix is an open-source software for automating the stages of data-analysis and data-visualization. After converting and uploading bibliographic data in R, Bibliometrix performs a descriptive analysis and different research-structure analysis.
Descriptive analysis provides some snapshots about the annual research development, the top “k” productive authors, papers, countries and most relevant keywords.
Main findings about the collection
#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 699
Average years from publication 2.96
Average citations per documents 20.21
Average citations per year per doc 4.516
References 32982
DOCUMENT TYPES
article 575
article; early access 42
article; proceedings paper 3
review 75
review; early access 4
DOCUMENT CONTENTS
Keywords Plus (ID) 1426
Author's Keywords (DE) 2493
AUTHORS
Authors 1672
Author Appearances 2285
Authors of single-authored documents 55
Authors of multi-authored documents 1617
AUTHORS COLLABORATION
Single-authored documents 64
Documents per Author 0.418
Authors per Document 2.39
Co-Authors per Documents 3.27
Collaboration Index 2.55
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 184
2022 20
Annual Percentage Growth Rate 18.10804
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 61.2 4.99
2 XIANG Z, 2015, INT J HOSP MANAG 10.1016/j.ijhm.2014.10.013 394 49.2 4.01
3 WOOD SA, 2013, SCI REP 10.1038/srep02976 339 33.9 3.01
4 GUO Y, 2017, TOURISM MANAGE 10.1016/j.tourman.2016.09.009 317 52.8 5.58
5 XIANG Z, 2017, TOURISM MANAGE 10.1016/j.tourman.2016.10.001 307 51.2 5.40
6 LI J, 2018, TOURISM MANAGE 10.1016/j.tourman.2018.03.009 246 49.2 8.51
7 BUHALIS D, 2015, J DESTIN MARK MANAG 10.1016/j.jdmm.2015.04.001 191 23.9 1.94
8 CARLOS GARCIA-PALOMARES J, 2015, APPL GEOGR 10.1016/j.apgeog.2015.08.002 180 22.5 1.83
9 CHENG M, 2019, INT J HOSP MANAG 10.1016/j.ijhm.2018.04.004 168 42.0 6.99
10 MARINE-ROIG E, 2015, J DESTIN MARK MANAG 10.1016/j.jdmm.2015.06.004 167 20.9 1.70
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 CHINA 199 0.2855 137 62 0.3116
2 USA 102 0.1463 65 37 0.3627
3 UNITED KINGDOM 59 0.0846 24 35 0.5932
4 SPAIN 52 0.0746 40 12 0.2308
5 KOREA 40 0.0574 27 13 0.3250
6 ITALY 36 0.0516 26 10 0.2778
7 AUSTRALIA 22 0.0316 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 2502 12.57
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 ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 17
9 JOURNAL OF DESTINATION MARKETING \\& MANAGEMENT 17
10 JOURNAL OF TRAVEL RESEARCH 17
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 BIG DATA 185 BIG DATA 211
2 TOURISM 64 TOURISM 119
3 SOCIAL MEDIA 51 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)
Most Cited References
CR699 <- citations(M699, field = "article", sep = ";")
cbind(CR699$Cited[1:20])
[,1]
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
Section 2: The Intellectual Structure of the field - Co-citation Analysis
Citation analysis is one of the main classic techniques in bibliometrics. It shows the structure of a specific field through the linkages between nodes (e.g. authors, papers, journal), while the edges can be differently interpretated depending on the network type, that are namely co-citation, direct citation, bibliographic coupling. Please see Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of analysis.
The useful dimensions to comment the co-citation networks are: (i) centrality and peripherality of nodes, (ii) their proximity and distance, (iii) strength of ties, (iv) clusters, (iiv) bridging contributions.
Third, a historiograph is built on direct citations. It draws the intellectual linkages in a historical order. Cited works of thousands of authors contained in a collection of published scientific articles is sufficient for recostructing the historiographic structure of the field, calling out the basic works in it.
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
NetMatrix699 <- biblioNetwork(M699, analysis = "co-citation", network = "references", sep = ";")
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)
Descriptive analysis of Article co-citation network characteristics
#netstat699 <- networkStat(NetMatrix699)
#summary(netstat699,k=10)
# it costs much item for this section. maybe there is something wrong, we may revise it later.
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)
Descriptive analysis of Journal co-citation network characteristics
netstat699tag <- networkStat(NetMatrix699tag)
summary(netstat699tag,k=10)
Main statistics about the network
Size 11911
Density 0.01
Transitivity 0.135
Diameter 4
Degree Centralization 0.774
Average path length 2.146
Section 3: Historiograph - Direct citation linkages
histresults699 <- histNetwork(M699, min.citations=quantile(M699$TC,0.75), sep = ";")
##
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
##
## Analyzing 47648 reference items...
##
## Found 172 documents with no empty Local Citations (LCS)
options(width = 130)
net699hist <- histPlot(histresults699, n=20, size = 5, labelsize = 3)
Legend
Label DOI Year LCS GCS
1 YANG Y, 2014, J TRAVEL RES DOI 10.1177/0047287513500391 10.1177/0047287513500391 2014 38 131
2 XIANG Z, 2015, INT J HOSP MANAG DOI 10.1016/J.IJHM.2014.10.013 10.1016/j.ijhm.2014.10.013 2015 123 394
3 GRETZEL U, 2015, ELECTRON MARK DOI 10.1007/S12525-015-0196-8 10.1007/s12525-015-0196-8 2015 31 490
4 SCHUCKERT M, 2015, INT J HOSP MANAG DOI 10.1016/J.IJHM.2014.12.007 10.1016/j.ijhm.2014.12.007 2015 25 81
5 PHILANDER K, 2016, INT J HOSP MANAG DOI 10.1016/J.IJHM.2016.02.001 10.1016/j.ijhm.2016.02.001 2016 19 82
6 MARIANI MM, 2016, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2015.12.008 10.1016/j.tourman.2015.12.008 2016 21 153
7 PAN B, 2017, J TRAVEL RES DOI 10.1177/0047287516669050 10.1177/0047287516669050 2017 26 84
8 XIANG Z, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.10.001 10.1016/j.tourman.2016.10.001 2017 70 307
9 LI X, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.07.005 10.1016/j.tourman.2016.07.005 2017 42 153
10 LIU Y, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.08.012 10.1016/j.tourman.2016.08.012 2017 52 117
11 GUO Y, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.09.009 10.1016/j.tourman.2016.09.009 2017 67 317
12 TALON-BALLESTERO P, 2018, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2018.03.017 10.1016/j.tourman.2018.03.017 2018 18 49
13 MARIANI MM, 2018, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2017.11.006 10.1016/j.tourman.2017.11.006 2018 22 64
14 ALAEI AR, 2019, J TRAVEL RES DOI 10.1177/0047287517747753 10.1177/0047287517747753 2019 38 150
15 MARIANI M, 2019, IEEE ACCESS DOI 10.1109/ACCESS.2018.2887300 10.1109/ACCESS.2018.2887300 2019 17 31
16 AHANI A, 2019, INT J HOSP MANAG DOI 10.1016/J.IJHM.2019.01.003 10.1016/j.ijhm.2019.01.003 2019 21 63
17 MARIANI M, 2020, TOUR REV DOI 10.1108/TR-06-2019-0259 10.1108/TR-06-2019-0259 2020 17 41
Section 4: The conceptual structure - Co-Word Analysis
Co-word networks show the conceptual structure, that uncovers links between concepts through term co-occurences.
Conceptual structure is often used to understand the topics covered by scholars (so-called research front) and identify what are the most important and the most recent issues.
Dividing the whole timespan in different timeslices and comparing the conceptual structures is useful to analyze the evolution of topics over time.
Bibliometrix is able to analyze keywords, but also the terms in the articles’ titles and abstracts. It does it using network analysis or correspondance analysis (CA) or multiple correspondance analysis (MCA). CA and MCA visualise the conceptual structure in a two-dimensional plot.
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)
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
Co-word Analysis through Correspondence Analysis
CS699 <- conceptualStructure(M699, method="MCA", field="ID", minDegree=10, clust=5, stemming=FALSE, labelsize=8,documents=20)
## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## Warning: ggrepel: 58 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## Warning: ggrepel: 60 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Section 5: Thematic Map
Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.
Please see Cobo, M. J., L?pez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.
Map699=thematicMap(M699, field = "ID", n = 250, minfreq = 5,
stemming = FALSE, size = 0.5, n.labels=3, repel = TRUE)
plot(Map699$map)
Cluster description
Clusters699=Map699$words[order(Map699$words$Cluster,-Map699$words$Occurrences),]
library(dplyr)
##
## 载入程辑包:'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
CL699 <- Clusters699 %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL699
## # A tibble: 27 x 5
## # Groups: Cluster_Label [5]
## Occurrences Words Cluster Color Cluster_Label
## <dbl> <chr> <dbl> <chr> <chr>
## 1 211 big data 1 #E41A1C80 big data
## 2 119 tourism 1 #E41A1C80 big data
## 3 100 hospitality 1 #E41A1C80 big data
## 4 94 social media 1 #E41A1C80 big data
## 5 93 impact 1 #E41A1C80 big data
## 6 75 management 2 #377EB880 management
## 7 53 performance 2 #377EB880 management
## 8 48 information 2 #377EB880 management
## 9 24 technology 2 #377EB880 management
## 10 20 framework 2 #377EB880 management
## # ... with 17 more rows
- 文章信息
- 作者: kaiwu
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The servitization in manufacturing may be the most important feature of Industry 4.0
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- 点击数:594
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