word2vec for text mining categories

Question

I have a list like this:

.NET
ABAP
Access
Account Management
Accounting
Active Directory
Agile Methodologies
Agile Project Management
AJAX
Algorithms
Analysis
Android
Android Development
AngularJS
Ant
Apache
ASP
ASP.NET
B2B
Banking
BPMN
Budgets
Business Analysis
Business Development
Business Intelligence
Business Planning
Business Process
Business Process Design
Business Process...

I would like to reduce the number of variables I have to analyze creating a more abstract categories. From the previous list every word is one variable for me.

I found that there is the word2vec but I can't find a CRAN documentation.

How can I use it? What could I do with this data?

Answer 1

Not word2vec, but have an alternative look at this post:

library(XML)
library(dplyr)
library(RecordLinkage)
df <- data.frame(words=capture.output(htmlParse("https://stackoverflow.com/questions/35904182/word2vec-for-text-mining-categories")[["//div/pre/code/text()"]]))
df %>% compare.dedup(strcmp = TRUE) %>%
             epiWeights() %>%
             epiClassify(0.8) %>%
             getPairs(show = "links", single.rows = TRUE) -> matches
left_join(mutate(df,ID = 1:nrow(df)), 
          select(matches,id1,id2) %>% arrange(id1) %>% filter(!duplicated(id2)), 
          by=c("ID"="id2")) %>%
    mutate(ID = ifelse(is.na(id1), ID, id1) ) %>%
    select(-id1) -> dfnew
head(dfnew, 30)
#                       words ID
# 1                      .NET  1
# 2                      ABAP  2
# 3                    Access  3
# 4        Account Management  4 # <--
# 5                Accounting  4 # <--
# 6          Active Directory  6
# 7       Agile Methodologies  7 # <--
# 8  Agile Project Management  7 # <--
# 9                      AJAX  9
# 10               Algorithms 10
# 11                 Analysis 11
# 12                  Android 12 # <--
# 13      Android Development 12 # <--
# 14                AngularJS 14
# 15                      Ant 15
# 16                   Apache 16
# 17                      ASP 17 # <--
# 18                  ASP.NET 17 # <--
# 19                      B2B 19
# 20                  Banking 20
# 21                     BPMN 21
# 22                  Budgets 22
# 23        Business Analysis 23 # <--
# 24     Business Development 23 # <--
# 25    Business Intelligence 23 # <--
# 26        Business Planning 23 # <--
# 27         Business Process 23 # <--
# 28  Business Process Design 23 # <--
# 29      Business Process... 23 # <--
# 30        Business Strategy 23 # <--

dfnew$ID may be your abstract category based on jaro-winkler string distances. May need some fine tuning though for your real data.