本文介绍了SVM用于R中的文本分类的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在使用SVM对我的文本进行分类，在这些情况下，我实际上并没有获得结果，而是获得了数值概率。
I am using SVM to classify my text where in i don't actually get the result instead get with numerical probabilities.
数据框（1:20训练组，21：50测试组）
已更新：
ou <- structure(list(text = structure(c(1L, 6L, 1L, 1L, 8L, 13L, 24L,
5L, 11L, 12L, 33L, 36L, 20L, 25L, 4L, 19L, 9L, 29L, 22L, 3L,
8L, 8L, 8L, 2L, 8L, 27L, 30L, 3L, 14L, 35L, 3L, 34L, 23L, 31L,
22L, 6L, 6L, 7L, 17L, 3L, 8L, 32L, 18L, 15L, 21L, 26L, 3L, 16L,
10L, 28L), .Label = c("access, access, access, access", "character(0)",
"report", "report, access", "report, access, access", "report, access, access, access",
"report, access, access, access, access, access, access", "report, access, access, access, access, access, access, access",
"report, access, access, access, access, access, access, report",
"report, access, access, access, access, access, report", "report, access, access, access, report",
"report, access, access, access, report, access", "report, access, access, report, access, access, access, access, access, access",
"report, data", "report, data, data", "report, data, data, data",
"report, data, data, data, data", "report, data, data, data, data, data",
"report, data, data, data, report, report, data, access,access",
"report, data, data, report", "report, data, report", "report, report",
"report, report, access, access, access", "report, report, access, access, report, report, report, report, report, report, data, data, report, access, report, report",
"report, report, access, report, report, report, report, report, data, data, report, access, report, report",
"report, report, access, report, report, report, report, report, report, data, data, report, access, report, report",
"report, report, data", "report, report, data, report", "report, report, report, data, report, report, data, data, report, data, data",
"report, report, report, report", "report, report, report, report, data, report, report, data, report, data, report",
"report, report, report, report, report, data, report, data, data",
"report, report, report, report, report, report, report", "report, report, report, report, report, report, report, access, access, access",
"report, report, report, report, report, report, report, report, data, data, report, access, report, report",
"report, report, report, report, report, report, report, report, report, report, data, report, report, report, report, report, report, report,report"
), class = "factor"), value = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("",
"Access", "Report/Data"), class = "factor")), .Names = c("text",
"value"), class = "data.frame", row.names = c(NA, -50L))
使用的代码：
Code used:
library(RTextTools)
doc_matrix <- create_matrix(ou$text, language="english", removeNumbers=TRUE, stemWords=TRUE, removeSparseTerms=.998)
#container <- create_container(doc_matrix, ou$text, trainSize=1:20, testSize=21:50, virgin=FALSE)
container <- create_container(doc_matrix, as.numeric(factor(ou$text)), trainSize=1:20, testSize=21:50, virgin=FALSE)
#Training models
SVM <- train_model(container,"SVM")
MAXENT <- train_model(container,"MAXENT")
BAGGING <- train_model(container,"BAGGING")
TREE <- train_model(container,"TREE")
#Classify data using trained models
SVM_CLASSIFY <- classify_model(container, SVM)
MAXENT_CLASSIFY <- classify_model(container, MAXENT)
BAGGING_CLASSIFY <- classify_model(container, BAGGING)
#Analytics
analytics <- create_analytics(container,SVM_CLASSIFY)
models <- train_models(container, algorithms=c("MAXENT","SVM"))
results <- classify_models(container, models)
analytics <- create_analytics(container, results)
summary(analytics)
SVM <- cross_validate(container, 5, "SVM")
write.csv(analytics@document_summary, "DocumentSummary.csv")
预期结果：
expected result:
text value
21 report, access, access, access, access, access, access, access Access
22 report, access, access, access, access, access, access, access Access
23 report, access, access, access, access, access, access, access Access
24 character(0) NA
25 report, access, access, access, access, access, access, access Access
26 report, report, data Report/Data
27 report, report, report, report Report/Data
28 report Report/Data
29 report, data Report/Data
30 report, report, report, report, report, report, report, report,
data, data, report, access, report, report Report/Data
结果是：
> MAXENTROPY_LABEL MAXENTROPY_PROB SVM_LABEL SVM_PROB MANUAL_CODE CONSENSUS_CODE CONSENSUS_AGREE CONSENSUS_INCORRECT PROBABILITY_CODE PROBABILITY_INCORRECT
> 1 8 0.999999066 22 0.070090645 8 8 1 0 8 0
> 2 8 0.999999066 22 0.070090645 8 8 1 0 8 0
> 3 8 0.999999066 22 0.070090645 8 8 1 0 8 0
> 4 1 0.055555556 12 0.071384112 2 12 1 1 12 1
> 5 8 0.999999066 22 0.070090645 8 8 1 0 8 0
> 6 25 1 12 0.074126949 27 25 1 1 25 1
> 7 33 0.627904676 13 0.068572857 30 33 1 1 33 1
> 8 33 0.406792176 12 0.074592181 3 33 1 1 33 1
> 9 20 1 12 0.074507793 14 20 1 1 20 1
编辑1：
我如何获得标签名称，而不是SVM标签编号。
EDIT 1:How can i achieve the label names instead of SVM label numbers.
推荐答案
我通常要做的是
ou <- cbind(ou$text, results)
并具有标签打印：
ou$value <- "NONE"
ou$value[results$SVM_LABEL=="1"] <- "Access"
ou$value[results$SVM_LABEL=="-1"] <- "Report/Data"
ou
（假设您在训练模型时使用了1和-1）
我知道这有点原始，但它很清楚并且可以正常工作
I know it's a little bit primitive but it's clear and works fine
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