R中两个数据帧之间句子的最接近匹配
我有两个数据帧。第一个-保存在名为b的对象中：R中两个数据帧之间句子的最接近匹配,r,string,match,R,String,Match,我有两个数据帧。第一个-保存在名为b的对象中： structure(list(CONTENT = c("@myntra beautiful teamä»ç where is the winners list?", "The best ever Puma wishlist for Workout freaks, Head over to @myntra https://t.co/V58Gk3EblW #MyPUMACollection Hit Like if you Find it good"
structure(list(CONTENT = c("@myntra beautiful teamä»ç where is the winners list?",
"The best ever Puma wishlist for Workout freaks, Head over to @myntra https://t.co/V58Gk3EblW #MyPUMACollection Hit Like if you Find it good",
"I finalised on buy a top from Myntra, and then I found the same top at 20% off in jabong. I feel like I've achieved so much in life!",
"Check out #myPUMAcollection on @Myntra. Its perfect for a day at gym. https://t.co/VeRy4G3c7X https://t.co/fOpBRWCdSh",
"Check out #myPUMAcollection on @Myntra. Its perfect for a day at gym. https://t.co/VeRy4G3c7X.....",
"@DrDrupad @myntra #myPUMAcollection superb :)", "Super exclusive collection @myntra #myPUMAcollection https://t.co/Qm9dZzJdms",
"@myntra gave my best Love playing wid u Hope to win #myPUMAcollection",
"Check out PUMA Unisex Black Running Performance Gloves on Myntra! https://t.co/YD6IcvuG98 @myntra #myPUMAcollection",
"@myntra i have been mailing my issue daily since past week.All i get in reply is an auto generated assurance mail. 1st time pissed wd myntra"
), score = c(7.129, 7.08, 6.676, 5.572, 5.572, 5.535, 5.424,
5.205, 4.464, 4.245)), .Names = c("CONTENT", "score"), row.names = c(25L,
103L, 95L, 66L, 90L, 75L, 107L, 32L, 184L, 2L), class = "data.frame")
structure(list(CONTENT = c("The best ever for workout over to myntra like if you find it good",
"i finalised buy a top myntra and found the at in feel like i so in life"
)), .Names = "CONTENT", row.names = c(103L, 95L), class = "data.frame")
cp <- str_split(c$CONTENT, " ")
library(data.table)
nn <- lengths(cp) ## Or, for < R-3.2.0, `nn <- sapply(wordList, length)`
dt <- data.table(grp=rep(seq_along(nn), times=nn), X = unlist(cp), key="grp")
dt[,Score:=b$score[pmatch(X,b$CONTENT)]]
dt[!is.na(Score), list(avgScore=sum(Score)), by="grp"]
cp这里有一种方法使用stringdist
包中的stringsim
。有几种方法（算法）可供选择——我选择了计算相似度的度量，因为它似乎能为数据生成合理的结果。话虽如此，我在这方面的经验充其量也只是一种偶然的体验，因此您可能需要花一些时间阅读并体验一下stringdist
提供的各种算法
为了减少混乱，我使用这个包装函数为给定字符串返回最相似（最高相似值）元素的索引
library(stringdist)
library(data.table)
best_match <- function(x, y, method = "jw", ...) {
which.max(stringsim(x, y, method, ...))
}
使用best_match
，添加一列，该列具有最佳匹配的索引（然后删除伪Idx
列）
并从dfu b
中提取相应的元素（我分别将数据从b
和c
重命名为dfu b
和df\u c
）：
下面是一种使用stringdist
包中的stringsim
的方法。有几种方法（算法）可供选择——我选择了计算相似度的度量，因为它似乎能为数据生成合理的结果。话虽如此，我在这方面的经验充其量也只是一种偶然的体验，因此您可能需要花一些时间阅读并体验一下stringdist
提供的各种算法
为了减少混乱，我使用这个包装函数为给定字符串返回最相似（最高相似值）元素的索引
library(stringdist)
library(data.table)
best_match <- function(x, y, method = "jw", ...) {
which.max(stringsim(x, y, method, ...))
}
使用best_match
，添加一列，该列具有最佳匹配的索引（然后删除伪Idx
列）
并从dfu b
中提取相应的元素（我分别将数据从b
和c
重命名为dfu b
和df\u c
）：
您是否致力于采用stru split
/pmatch
的方法来确定给定短语的最佳匹配？因为在这种情况下，有合适的模糊匹配算法可以产生更好的结果。@nrussell不是真的……如果你能让我知道可以部署的模糊匹配算法的种类，那会很有帮助。如果你致力于使用stru split
/pmatch
的方法来确定一个对象的最佳匹配给定的短语？因为在这种情况下，有合适的模糊匹配算法可以产生更好的结果。@nrussell不是真的……如果您能让我知道可以部署的模糊匹配算法的种类，我会很有帮助的。谢谢nrussell……它在示例集上完美地工作了。我将进一步探讨如何用我的实际数据集实现这一点。再次感谢。@nrussell…我确实通过了Jaro距离…发现非常有趣…感谢你介绍我模糊匹配算法…以前从未知道…将对我非常有帮助。非常感谢nrussell…它与示例集完美结合。我将进一步探讨如何用我的实际数据集实现这一点。再次感谢。@nrussell…我确实通过了Jaro距离…发现非常有趣…感谢你介绍我模糊匹配算法…以前从未知道…将对我非常有帮助。
Dt[, MatchIdx := best_match(df_b$CONTENT, MatchPhrase),
by = "Idx"][,Idx := NULL]
Dt[, .(Score = df_b$score[MatchIdx],
BestMatch = df_b$CONTENT[MatchIdx]),
by = "MatchPhrase"]
# MatchPhrase Score
#1: The best ever for workout over to myntra like if you find it good 7.080
#2: i finalised buy a top myntra and found the at in feel like i so in life 6.676
# BestMatch
#1: The best ever Puma wishlist for Workout freaks, Head over to @myntra https://t.co/V58Gk3EblW #MyPUMACollection Hit Like if you Find it good
#2: I finalised on buy a top from Myntra, and then I found the same top at 20% off in jabong. I feel like I've achieved so much in life!