results/text_mining_pe.md
In f0nzie/petroleum.ml: Machine Learning Applications in Machine Learning
title: "Text Mining for Petroleum Engineering"
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Last week I was talking with a colleague about some projects that could be deployed using R, and then the topic of well technical potential came up. I got hooked by the subject, and then decided to explore it a little bit more.
What is not better if going to OnePetro and search for papers on technical potential. I did and found 132 papers matching the term. Well, I was not going to purchase those 132 papers, you know. I had to reduce that number to an essential minimum. How?
That is the topic of today's article. And for that purpose, we will be using text mining.
The Toolbox
I will be using the following ingredients for this recipe. :)
All of these tools are open source and free. Never has been more true saying "free ride" for studying, learning and using state-of-the-art software than today. You will just have to invest some of your time.
How many papers are there?
Let's find out how many papers contain the term technical potential. Here is the code in R:
library(petro.One)
my_url <- make_search_url(query = "technical potential",
how = "all")
num_papers <- get_papers_count(my_url)
# [1] 132
What the package petro.One does is connecting with OnePetro and submit the text technical potential to the website, specifying that we want papers that only have those two words together with the parameter how. We could read the number of papers from the object num_papers.
We read the titles of the papers, and the rest of the metadata, and put that in a dataframe df. So, we get 132 papers as it is shown below.
df <- read_multidoc(my_url)
write.csv(df, file.path(rprojroot::find_rstudio_root_file(), "data", "tp-papers.csv"), row.names = FALSE)
df
# A tibble: 134 x 6
title_data paper_id source type year author1_data
<chr> <chr> <chr> <chr> <int> <chr>
1 Reserve Technical Po… " … " … " … 2014 Ruslan, M.,
2 Technical Potential:… " … " … " … 2017 Gupta, Vipin Pra…
3 Using Business Intel… " … " … " … 2017 Haris Hamzah, M,…
4 Managed Pressure Dri… " … " … " … 2013 Sridharan, Prem …
5 Optimizing Number of… " … " … " … 2015 Guk, Vyacheslav,…
6 Intelligent Automati… " … " … " … 2014 Zubarev, Victor,…
7 The Models of Sea Wa… " … " … " … 2012 Chizhiumov, Serg…
8 The First Subsea Com… " … " … " … 2013 Mahadi, K., PETR…
9 Staying Ahead throug… " … " … " … 2017 Baghdadi, F., PE…
10 Optimization of Surf… " … " … " … 2017 Lotfollahi, Moha…
# ... with 124 more rows
How many papers with "technical" and "potential"
We assume that the papers with stronger technical potential content are those that have in the title those words. We will look at papers in the title.
df$title <- tolower(df$title_data)
# df[grep("technical potential", df$title), c("title_data", "paper_id")]
df[grep("technical potential", df$title), ]
# A tibble: 3 x 7
title_data paper_id source type year author1_data title
<chr> <chr> <chr> <chr> <int> <chr> <chr>
1 Reserve Technic… " … " … " … 2014 Ruslan, M., reserve t…
2 Technical Poten… " … " … " … 2017 Gupta, Vipin… technical…
3 Technical Poten… " … " … " … 1982 Quong, Rolan… technical…
There are 3 papers that have technical potential in their title.
At this point, we could do two things:
- retrieve the papers that have
technical potential in the title
- perform an additional 1-word and 2-word terms analysis on the 134 that match our query.
We will do first a text mining of the papers that gave a match on the title.
- What grep does is finding a word pattern in the column title of the dataframe
df. There are 3 papers that match that pattern.
df[grep("technical potential", df$title), c("source", "paper_id")]
# A tibble: 3 x 2
source paper_id
<chr> <chr>
1 " SPE " " 169835-MS "
2 " SPE " " 187429-MS "
3 " SPE " " 9469-PA "
These three papers are our best candidates, from the 132 papers that matched our initial query. Next is finding which of the three papers is the one richer in technical potential content and focus our attention to it.
What we will do is reading inside the papers, the PDF files.
Data Mining the PDF files
Once the papers have been downloaded, we will verify that they are in our working directory:
# papers to analyze under data/papers
# provide the full path for the next function
files <- list.files(path = file.path(rprojroot::find_rstudio_root_file(),
"/data/papers"),
pattern = ".pdf$",
full.names = TRUE)
files
[1] "/home/superuser/github-oilgains/petroleum.ml//data/papers/gupta2017.pdf"
[2] "/home/superuser/github-oilgains/petroleum.ml//data/papers/quong1982.pdf"
[3] "/home/superuser/github-oilgains/petroleum.ml//data/papers/ruslan2014.pdf"
That gives us three PDF files. Pay attention to the object files. We will use it to create the mining corpus in a moment.
Read the PDF files and inspect the corpus
The following operation is reading the papers that are in PDF format. R has a way to read PDF files through the function readPDF of the package tm.
library(tm)
if (!require(pdftools)) install.packages("pdftools")
Rpdf <- readPDF(control = list(text = "-layout"))
papers <- Corpus(URISource(files),
readerControl = list(reader = Rpdf))
papers <- tm_map(papers, content_transformer(function(x) iconv(enc2utf8(x),
sub = "byte")))
inspect(papers)
# one-word terms in gupta2017 paper
papers.tdm <- TermDocumentMatrix(papers,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
removeNumbers = TRUE,
tolower = TRUE
#stemming = TRUE
))
inspect(papers.tdm)
<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 3
[[1]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 57572
[[2]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 49884
[[3]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 27163
<<TermDocumentMatrix (terms: 2609, documents: 3)>>
Non-/sparse entries: 3374/4453
Sparsity : 57%
Maximal term length: 46
Weighting : term frequency (tf)
Sample :
Docs
Terms gupta2017.pdf quong1982.pdf ruslan2014.pdf
data 90 10 11
field 30 0 23
fields 48 0 5
forecast 72 0 0
forecasting 67 0 0
potential 61 7 47
process 30 18 3
production 89 7 28
technical 55 5 40
well 45 7 10
The papers object is the corpus. The object papers.tdm is the term document matrix.
The table at the bottom is the term document matrix. A matrix where the rows are the terms and the columns are the counts of those terms for each paper.
Observe that each of the PDF files has an identifier like this [[1]], [[2]] and [[3]]. Take a look at the number of characters each document contains.
Now, let's get the number of words or terms
# how many terms per paper
sapply(papers, function(x) length(termFreq(x)) )
gupta2017.pdf quong1982.pdf ruslan2014.pdf
1939 1703 1058
Now, a summary table of our initial findings:
Docs gupta2017.pdf quong1982.pdf ruslan2014.pdf
Num Chars 58230 50343 27492
Terms 2274 1878 1207
We can see that the document with more content is gupta2017, the second quong1982, and the third, ruslan2014. But we will find something interesting later.
Find the most frequent terms in the papers
Now that the papers corpus has been converted to a term document matrix, we could continue with finding the most frequent terms:
# findFreqTerms(papers.tdm, lowfreq = 50, highfreq = Inf)
findMostFreqTerms(papers.tdm)
$gupta2017.pdf
data production forecast forecasting potential technical
90 89 72 67 61 55
$quong1982.pdf
brine pressure solubility injection gas stripping
44 41 34 28 27 26
$ruslan2014.pdf
potential reserves technical production field will
47 45 40 28 23 21
What is happening here is that even though "technical potential" is in the title of quong1982, the paper is not strong in technical potential, per se. The other two papers are stronger.
Observe the frequency of the terms. What is happening here is that even though "technical potential" is found in the title of the paper quong1982, the paper is not rich in technical potential terms. The other two papers are stronger. We will put aside quong1982 and analyze gupta2017 and ruslan2014.
Just to be sure, one more time let's find the score of all the papers given the terms "technical" and "potential".
tm_term_score(papers.tdm, "technical")
tm_term_score(papers.tdm, "potential")
gupta2017.pdf quong1982.pdf ruslan2014.pdf
55 5 40
gupta2017.pdf quong1982.pdf ruslan2014.pdf
61 7 47
Well, that confirms our initial analysis; quong1982 is not the best candidate paper for studying "technical potential".
Term frequency analysis for the first paper
What we do here is building a dataframe of terms vs. frequency at which each occur.
library(tibble)
# frequency analysis of gupta2017
# theFile <- "gupta2017.pdf"
theFile <- files[1]
paper <- Corpus(URISource(theFile),
readerControl = list(reader = Rpdf))
paper <- tm_map(paper, content_transformer(function(x) iconv(enc2utf8(x),
sub = "byte")))
paper.tdm <- TermDocumentMatrix(paper,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
removeNumbers = TRUE,
tolower = TRUE
#stemming = TRUE
))
findFreqTerms(paper.tdm, lowfreq = 50, highfreq = Inf)
findMostFreqTerms(paper.tdm)
tdm.matrix <- as.matrix(paper.tdm)
tdm.rs <- sort(rowSums(tdm.matrix), decreasing = TRUE)
tdm.df1 <- tibble(word = names(tdm.rs), freq = tdm.rs)
tdm.df1
[1] "data" "forecast" "forecasting" "potential" "production"
[6] "technical"
$gupta2017.pdf
data production forecast forecasting potential technical
90 89 72 67 61 55
# A tibble: 1,442 x 2
word freq
<chr> <dbl>
1 data 90.
2 production 89.
3 forecast 72.
4 forecasting 67.
5 potential 61.
6 technical 55.
7 model 49.
8 fields 48.
9 well 45.
10 based 39.
# ... with 1,432 more rows
Term freqency analysis for the second paper
Now, for the second paper:
# frequency analysis of ruslan2014
# theFile <- "ruslan2014.pdf"
theFile <- files[3]
paper <- Corpus(URISource(theFile),
readerControl = list(reader = Rpdf))
paper <- tm_map(paper, content_transformer(function(x) iconv(enc2utf8(x),
sub = "byte")))
paper.tdm <- TermDocumentMatrix(paper,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
tolower = TRUE,
removeNumbers = TRUE
#stemming = TRUE
))
findFreqTerms(paper.tdm, lowfreq = 50, highfreq = Inf)
findMostFreqTerms(paper.tdm)
tdm.matrix <- as.matrix(paper.tdm)
tdm.rs <- sort(rowSums(tdm.matrix), decreasing = TRUE)
tdm.df2 <- tibble(word = names(tdm.rs), freq = tdm.rs)
tdm.df2
character(0)
$ruslan2014.pdf
potential reserves technical production field will
47 45 40 28 23 21
# A tibble: 809 x 2
word freq
<chr> <dbl>
1 potential 47.
2 reserves 45.
3 technical 40.
4 production 28.
5 field 23.
6 will 21.
7 resources 19.
8 management 18.
9 mature 18.
10 proved 17.
# ... with 799 more rows
We can see some differences between the two: such as the total number of terms or words, the frequency for each term, and that the terms and frequency differ in both documents.
To finish, let's plot terms vs frequency for both papers:
library(ggplot2)
p1 <- ggplot(subset(tdm.df1, freq > 30), aes(x=word, y=freq)) +
geom_bar(stat = "identity", width = 0.8) +
xlab("Terms") + ylab("Count") + ggtitle("gupta2017") +
coord_flip()
p2 <- ggplot(subset(tdm.df2, freq > 30), aes(x=reorder(word, freq), y=freq)) +
geom_bar(stat = "identity", width = 0.2) +
xlab("Terms") + ylab("Count") + ggtitle("ruslan2014") +
coord_flip()
require("gridExtra")
grid.arrange(arrangeGrob(p1, p2))
Conclusion
-
We rapidly determined, in our study for the term "technical potential", which papers are the best candidates.
-
We used text mining, composed a document corpus and a term document matrix, to take that decision, and narrow down from 132 to 3 papers that search and the acquisition of those papers.
-
From the three selected papers, we were able to put aside one because the other two papers were even richer in the content we were looking for.
This doesn't mean that we should discard reading the other 130 papers; but doing a deeper analysis of all the papers requires purchasing and downloading all of them. If this option is viable, we may find that other papers may contain interesting content for the subject of our research. In the case of our study for reasons of time and budget we came up rapidly to only those two papers.
References
f0nzie/petroleum.ml documentation built on May 12, 2019, 6:22 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Text Mining for Petroleum Engineering" output: html_document: df_print: paged toc: yes html_notebook: toc: yes pdf_document: toc: yes editor_options: chunk_output_type: inline
Last week I was talking with a colleague about some projects that could be deployed using R, and then the topic of well technical potential came up. I got hooked by the subject, and then decided to explore it a little bit more.
What is not better if going to OnePetro and search for papers on technical potential. I did and found 132 papers matching the term. Well, I was not going to purchase those 132 papers, you know. I had to reduce that number to an essential minimum. How?
That is the topic of today's article. And for that purpose, we will be using text mining.
The Toolbox
I will be using the following ingredients for this recipe. :)
All of these tools are open source and free. Never has been more true saying "free ride" for studying, learning and using state-of-the-art software than today. You will just have to invest some of your time.
How many papers are there?
Let's find out how many papers contain the term technical potential. Here is the code in R:
library(petro.One)
my_url <- make_search_url(query = "technical potential",
how = "all")
num_papers <- get_papers_count(my_url)
# [1] 132
What the package petro.One does is connecting with OnePetro and submit the text technical potential to the website, specifying that we want papers that only have those two words together with the parameter how. We could read the number of papers from the object num_papers.
We read the titles of the papers, and the rest of the metadata, and put that in a dataframe df. So, we get 132 papers as it is shown below.
df <- read_multidoc(my_url)
write.csv(df, file.path(rprojroot::find_rstudio_root_file(), "data", "tp-papers.csv"), row.names = FALSE)
df
# A tibble: 134 x 6
title_data paper_id source type year author1_data
<chr> <chr> <chr> <chr> <int> <chr>
1 Reserve Technical Po… " … " … " … 2014 Ruslan, M.,
2 Technical Potential:… " … " … " … 2017 Gupta, Vipin Pra…
3 Using Business Intel… " … " … " … 2017 Haris Hamzah, M,…
4 Managed Pressure Dri… " … " … " … 2013 Sridharan, Prem …
5 Optimizing Number of… " … " … " … 2015 Guk, Vyacheslav,…
6 Intelligent Automati… " … " … " … 2014 Zubarev, Victor,…
7 The Models of Sea Wa… " … " … " … 2012 Chizhiumov, Serg…
8 The First Subsea Com… " … " … " … 2013 Mahadi, K., PETR…
9 Staying Ahead throug… " … " … " … 2017 Baghdadi, F., PE…
10 Optimization of Surf… " … " … " … 2017 Lotfollahi, Moha…
# ... with 124 more rows
How many papers with "technical" and "potential"
We assume that the papers with stronger technical potential content are those that have in the title those words. We will look at papers in the title.
df$title <- tolower(df$title_data)
# df[grep("technical potential", df$title), c("title_data", "paper_id")]
df[grep("technical potential", df$title), ]
# A tibble: 3 x 7
title_data paper_id source type year author1_data title
<chr> <chr> <chr> <chr> <int> <chr> <chr>
1 Reserve Technic… " … " … " … 2014 Ruslan, M., reserve t…
2 Technical Poten… " … " … " … 2017 Gupta, Vipin… technical…
3 Technical Poten… " … " … " … 1982 Quong, Rolan… technical…
There are 3 papers that have technical potential in their title.
At this point, we could do two things:
- retrieve the papers that have
technical potentialin the title - perform an additional 1-word and 2-word terms analysis on the 134 that match our query.
We will do first a text mining of the papers that gave a match on the title.
- What grep does is finding a word pattern in the column title of the dataframe
df. There are 3 papers that match that pattern.
df[grep("technical potential", df$title), c("source", "paper_id")]
# A tibble: 3 x 2
source paper_id
<chr> <chr>
1 " SPE " " 169835-MS "
2 " SPE " " 187429-MS "
3 " SPE " " 9469-PA "
These three papers are our best candidates, from the 132 papers that matched our initial query. Next is finding which of the three papers is the one richer in technical potential content and focus our attention to it.
What we will do is reading inside the papers, the PDF files.
Data Mining the PDF files
Once the papers have been downloaded, we will verify that they are in our working directory:
# papers to analyze under data/papers
# provide the full path for the next function
files <- list.files(path = file.path(rprojroot::find_rstudio_root_file(),
"/data/papers"),
pattern = ".pdf$",
full.names = TRUE)
files
[1] "/home/superuser/github-oilgains/petroleum.ml//data/papers/gupta2017.pdf"
[2] "/home/superuser/github-oilgains/petroleum.ml//data/papers/quong1982.pdf"
[3] "/home/superuser/github-oilgains/petroleum.ml//data/papers/ruslan2014.pdf"
That gives us three PDF files. Pay attention to the object files. We will use it to create the mining corpus in a moment.
Read the PDF files and inspect the corpus
The following operation is reading the papers that are in PDF format. R has a way to read PDF files through the function readPDF of the package tm.
library(tm)
if (!require(pdftools)) install.packages("pdftools")
Rpdf <- readPDF(control = list(text = "-layout"))
papers <- Corpus(URISource(files),
readerControl = list(reader = Rpdf))
papers <- tm_map(papers, content_transformer(function(x) iconv(enc2utf8(x),
sub = "byte")))
inspect(papers)
# one-word terms in gupta2017 paper
papers.tdm <- TermDocumentMatrix(papers,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
removeNumbers = TRUE,
tolower = TRUE
#stemming = TRUE
))
inspect(papers.tdm)
<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 3
[[1]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 57572
[[2]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 49884
[[3]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 27163
<<TermDocumentMatrix (terms: 2609, documents: 3)>>
Non-/sparse entries: 3374/4453
Sparsity : 57%
Maximal term length: 46
Weighting : term frequency (tf)
Sample :
Docs
Terms gupta2017.pdf quong1982.pdf ruslan2014.pdf
data 90 10 11
field 30 0 23
fields 48 0 5
forecast 72 0 0
forecasting 67 0 0
potential 61 7 47
process 30 18 3
production 89 7 28
technical 55 5 40
well 45 7 10
The papers object is the corpus. The object papers.tdm is the term document matrix.
The table at the bottom is the term document matrix. A matrix where the rows are the terms and the columns are the counts of those terms for each paper.
Observe that each of the PDF files has an identifier like this [[1]], [[2]] and [[3]]. Take a look at the number of characters each document contains.
Now, let's get the number of words or terms
# how many terms per paper
sapply(papers, function(x) length(termFreq(x)) )
gupta2017.pdf quong1982.pdf ruslan2014.pdf
1939 1703 1058
Now, a summary table of our initial findings:
Docs gupta2017.pdf quong1982.pdf ruslan2014.pdf
Num Chars 58230 50343 27492
Terms 2274 1878 1207
We can see that the document with more content is gupta2017, the second quong1982, and the third, ruslan2014. But we will find something interesting later.
Find the most frequent terms in the papers
Now that the papers corpus has been converted to a term document matrix, we could continue with finding the most frequent terms:
# findFreqTerms(papers.tdm, lowfreq = 50, highfreq = Inf)
findMostFreqTerms(papers.tdm)
$gupta2017.pdf
data production forecast forecasting potential technical
90 89 72 67 61 55
$quong1982.pdf
brine pressure solubility injection gas stripping
44 41 34 28 27 26
$ruslan2014.pdf
potential reserves technical production field will
47 45 40 28 23 21
What is happening here is that even though "technical potential" is in the title of quong1982, the paper is not strong in technical potential, per se. The other two papers are stronger.
Observe the frequency of the terms. What is happening here is that even though "technical potential" is found in the title of the paper quong1982, the paper is not rich in technical potential terms. The other two papers are stronger. We will put aside quong1982 and analyze gupta2017 and ruslan2014.
Just to be sure, one more time let's find the score of all the papers given the terms "technical" and "potential".
tm_term_score(papers.tdm, "technical")
tm_term_score(papers.tdm, "potential")
gupta2017.pdf quong1982.pdf ruslan2014.pdf
55 5 40
gupta2017.pdf quong1982.pdf ruslan2014.pdf
61 7 47
Well, that confirms our initial analysis; quong1982 is not the best candidate paper for studying "technical potential".
Term frequency analysis for the first paper
What we do here is building a dataframe of terms vs. frequency at which each occur.
library(tibble)
# frequency analysis of gupta2017
# theFile <- "gupta2017.pdf"
theFile <- files[1]
paper <- Corpus(URISource(theFile),
readerControl = list(reader = Rpdf))
paper <- tm_map(paper, content_transformer(function(x) iconv(enc2utf8(x),
sub = "byte")))
paper.tdm <- TermDocumentMatrix(paper,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
removeNumbers = TRUE,
tolower = TRUE
#stemming = TRUE
))
findFreqTerms(paper.tdm, lowfreq = 50, highfreq = Inf)
findMostFreqTerms(paper.tdm)
tdm.matrix <- as.matrix(paper.tdm)
tdm.rs <- sort(rowSums(tdm.matrix), decreasing = TRUE)
tdm.df1 <- tibble(word = names(tdm.rs), freq = tdm.rs)
tdm.df1
[1] "data" "forecast" "forecasting" "potential" "production"
[6] "technical"
$gupta2017.pdf
data production forecast forecasting potential technical
90 89 72 67 61 55
# A tibble: 1,442 x 2
word freq
<chr> <dbl>
1 data 90.
2 production 89.
3 forecast 72.
4 forecasting 67.
5 potential 61.
6 technical 55.
7 model 49.
8 fields 48.
9 well 45.
10 based 39.
# ... with 1,432 more rows
Term freqency analysis for the second paper
Now, for the second paper:
# frequency analysis of ruslan2014
# theFile <- "ruslan2014.pdf"
theFile <- files[3]
paper <- Corpus(URISource(theFile),
readerControl = list(reader = Rpdf))
paper <- tm_map(paper, content_transformer(function(x) iconv(enc2utf8(x),
sub = "byte")))
paper.tdm <- TermDocumentMatrix(paper,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
tolower = TRUE,
removeNumbers = TRUE
#stemming = TRUE
))
findFreqTerms(paper.tdm, lowfreq = 50, highfreq = Inf)
findMostFreqTerms(paper.tdm)
tdm.matrix <- as.matrix(paper.tdm)
tdm.rs <- sort(rowSums(tdm.matrix), decreasing = TRUE)
tdm.df2 <- tibble(word = names(tdm.rs), freq = tdm.rs)
tdm.df2
character(0)
$ruslan2014.pdf
potential reserves technical production field will
47 45 40 28 23 21
# A tibble: 809 x 2
word freq
<chr> <dbl>
1 potential 47.
2 reserves 45.
3 technical 40.
4 production 28.
5 field 23.
6 will 21.
7 resources 19.
8 management 18.
9 mature 18.
10 proved 17.
# ... with 799 more rows
We can see some differences between the two: such as the total number of terms or words, the frequency for each term, and that the terms and frequency differ in both documents.
To finish, let's plot terms vs frequency for both papers:
library(ggplot2)
p1 <- ggplot(subset(tdm.df1, freq > 30), aes(x=word, y=freq)) +
geom_bar(stat = "identity", width = 0.8) +
xlab("Terms") + ylab("Count") + ggtitle("gupta2017") +
coord_flip()
p2 <- ggplot(subset(tdm.df2, freq > 30), aes(x=reorder(word, freq), y=freq)) +
geom_bar(stat = "identity", width = 0.2) +
xlab("Terms") + ylab("Count") + ggtitle("ruslan2014") +
coord_flip()
require("gridExtra")
grid.arrange(arrangeGrob(p1, p2))
Conclusion
-
We rapidly determined, in our study for the term "technical potential", which papers are the best candidates.
-
We used text mining, composed a document corpus and a term document matrix, to take that decision, and narrow down from 132 to 3 papers that search and the acquisition of those papers.
-
From the three selected papers, we were able to put aside one because the other two papers were even richer in the content we were looking for.
This doesn't mean that we should discard reading the other 130 papers; but doing a deeper analysis of all the papers requires purchasing and downloading all of them. If this option is viable, we may find that other papers may contain interesting content for the subject of our research. In the case of our study for reasons of time and budget we came up rapidly to only those two papers.
References
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