我正在尝试将单词列表与句子列表进行匹配，并使用匹配的单词和句子形成数据框。例如：
words <- c("far better","good","great","sombre","happy")
sentences <- c("This document is far better","This is a great app","The night skies were sombre and starless", "The app is too good and i am happy using it", "This is how it works")
预期结果（数据帧）如下：
sentences words
This document is far better better
This is a great app great
The night skies were sombre and starless sombre
The app is too good and i am happy using it good, happy
This is how it works -
我使用以下代码来实现此目的。
lengthOfData <- nrow(sentence_df)
pos.words <- polarity_table[polarity_table$y>0]$x
neg.words <- polarity_table[polarity_table$y<0]$x
positiveWordsList <- list()
negativeWordsList <- list()
for(i in 1:lengthOfData){
sentence <- sentence_df[i,]$comment
#sentence <- gsub('[[:punct:]]', "", sentence)
#sentence <- gsub('[[:cntrl:]]', "", sentence)
#sentence <- gsub('\\d+', "", sentence)
sentence <- tolower(sentence)
# get unigrams from the sentence
unigrams <- unlist(strsplit(sentence, " ", fixed=TRUE))
# get bigrams from the sentence
bigrams <- unlist(lapply(1:length(unigrams)-1, function(i) {paste(unigrams[i],unigrams[i+1])} ))
# .. and combine into data frame
words <- c(unigrams, bigrams)
#if(sentence_df[i,]$ave_sentiment)
pos.matches <- match(words, pos.words)
neg.matches <- match(words, neg.words)
pos.matches <- na.omit(pos.matches)
neg.matches <- na.omit(neg.matches)
positiveList <- pos.words[pos.matches]
negativeList <- neg.words[neg.matches]
if(length(positiveList)==0){
positiveList <- c("-")
}
if(length(negativeList)==0){
negativeList <- c("-")
}
negativeWordsList[i]<- paste(as.character(unique(negativeList)), collapse=", ")
positiveWordsList[i]<- paste(as.character(unique(positiveList)), collapse=", ")
positiveWordsList[i] <- sapply(positiveWordsList[i], function(x) toString(x))
negativeWordsList[i] <- sapply(negativeWordsList[i], function(x) toString(x))
}
positiveWordsList <- as.vector(unlist(positiveWordsList))
negativeWordsList <- as.vector(unlist(negativeWordsList))
scores.df <- data.frame(ave_sentiment=sentence_df$ave_sentiment, comment=sentence_df$comment,pos=positiveWordsList,neg=negativeWordsList, year=sentence_df$year,month=sentence_df$month,stringsAsFactors = FALSE)
我有28k句和65k字匹配。上面的代码需要45秒才能完成任务。有关如何提高代码性能的任何建议都需要花费大量时间吗？
修改
我想只得到那些与句子中的单词完全匹配的单词。例如：
words <- c('sin','vice','crashes')
sentences <- ('Since the app crashes frequently, I advice you guys to fix the issue ASAP')
现在针对上述情况，我的输出应该如下：
sentences words
Since the app crashes frequently, I advice you guys to fix crahses
the issue ASAP
答案 0 :(得分：1)
我能够使用@David Arenburg的答案进行一些修改。这就是我做的。我使用以下（由David建议）来形成数据框。
if(!($stmtUpdate = $con->prepare("UPDATE user SET avatar = ? WHERE user_name = ?"))) {
echo "Prepare failed: (" . $con->errno . ")" . $con->error;
}
if(!($stmtInsert = $con->prepare("INSERT INTO user ( avatar ) VALUES ( ? )"))) {
echo "Prepare failed: (" . $con->errno . ")" . $con->error;
}
if(!($stmtSelect = $con->prepare("SELECT * FROM user WHERE user_name = ? "))) {
echo "Prepare failed: (" . $con->errno . ")" . $con->error;
}
if(!$stmt->bind_param('sss', $temp, $NewImageName, $temp)) {
echo "Binding paramaters failed:(" . $stmt->errno . ")" . $stmt->error;
}
if(!$stmt->execute()){
echo "Execute failed: (" . $stmt->errno .")" . $stmt->error;
}
$stmt->store_result();
if($stmt->num_rows == 0) {
if(!empty($_FILES['ImageFile']['name'])){
$con->prepare($stmtUpdate)or die(mysqli_error($con));
header("location:edit-profile.php?user_name=$temp");
exit;
}
} else {
$stmt->bind_result($avatar, $avatar, $temp);
$stmt->fetch();
header("location:edit-profile.php?user_name=$temp");
}
$stmt->close();
上述方法的问题在于它没有完全匹配单词。 因此，我使用以下内容过滤掉与句子中的单词不完全匹配的单词。
df <- data.frame(sentences) ;
df$words <- sapply(sentences, function(x) toString(words[stri_detect_fixed(x, words)]))
应用上述行后，输出数据帧会发生如下变化。
df <- data.frame(fil=unlist(s),text=rep(df$sentence, sapply(s, FUN=length)))
现在将以下过滤器应用于数据框，以删除与句子中出现的字词不完全匹配的字词。
sentences words
This document is far better better
This is a great app great
The night skies were sombre and starless sombre
The app is too good and i am happy using it good
The app is too good and i am happy using it happy
This is how it works -
Since the app crashes frequently, I advice you guys to fix
the issue ASAP crahses
Since the app crashes frequently, I advice you guys to fix
the issue ASAP vice
Since the app crashes frequently, I advice you guys to fix
the issue ASAP sin
现在我的结果数据框如下。
df <- df[apply(df, 1, function(x) tolower(x[1]) %in% tolower(unlist(strsplit(x[2], split='\\s+')))),]
stri_detect_fixed减少了我的计算时间。剩下的过程并没有花费太多时间。感谢@David指出我正确的方向。
答案 1 :(得分：0)
您可以使用extract_sentiment_terms在最新版本的 sentimentr 中执行此操作，但您必须首先制作情感键并为字词指定值：
pos <- c("far better","good","great","sombre","happy")
neg <- c('sin','vice','crashes')
sentences <- c('Since the app crashes frequently, I advice you guys to fix the issue ASAP',
"This document is far better", "This is a great app","The night skies were sombre and starless",
"The app is too good and i am happy using it", "This is how it works")
library(sentimentr)
(sentkey <- as_key(data.frame(c(pos, neg), c(rep(1, length(pos)), rep(-1, length(neg))), stringsAsFactors = FALSE)))
## x y
## 1: crashes -1
## 2: far better 1
## 3: good 1
## 4: great 1
## 5: happy 1
## 6: sin -1
## 7: sombre 1
## 8: vice -1
extract_sentiment_terms(sentences, sentkey)
## element_id sentence_id negative positive
## 1: 1 1 crashes
## 2: 2 1 far better
## 3: 3 1 great
## 4: 4 1 sombre
## 5: 5 1 good,happy
## 6: 6 1