r - 尝试将单词列表与 R 中的句子列表匹配时出现性能问题

Question

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(一个dataframe)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                                      -

我正在使用以下代码来实现这一点。

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 个句子和 65k 个单词要匹配。上面的代码需要 45 秒才能完成任务。关于如何提高代码性能的任何建议，因为当前方法需要花费大量时间？

编辑:

我只想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')

现在对于上述情况，我的输出应该如下所示:

sentences                                                           words
Since the app crashes frequently, I advice you guys to fix        crahses
the issue ASAP  

Answer 1

我能够使用@David Arenburg 的答案进行一些修改。这是我所做的。我使用以下内容(David 建议)来形成数据框。

df <- data.frame(sentences) ; 
df$words <- sapply(sentences, function(x) toString(words[stri_detect_fixed(x, words)]))

上述方法的问题在于它没有进行精确的单词匹配。所以我用下面的过滤掉了和句子中的词不完全匹配的词。

df <- data.frame(fil=unlist(s),text=rep(df$sentence, sapply(s, FUN=length)))

应用上述代码后，输出数据框发生如下变化。

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

现在将以下过滤器应用于数据框，以删除与句子中出现的那些词不完全匹配的那些词。

df <- df[apply(df, 1, function(x) tolower(x[1]) %in% tolower(unlist(strsplit(x[2], split='\\s+')))),]

现在我生成的数据框如下所示。

    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

stri_detect_fixed 大大减少了我的计算时间。剩下的过程并没有占用太多时间。感谢@David 为我指明了正确的方向。