R 按日期合并推文

我希望这不是一个基本的问题， 我有一个tweets的数据帧（在R中）。 我的目标是按日期计算情绪

如果有人能给我建议，我将不胜感激， 如何按日期连接tweets

tweet$text

，其中 每个观察结果都会变成一个合并的tweet/文本字符串

例如，如果我有：

Created_Date       Tweet

2014-01-04         "the iphone is magnificent"

2014-01-04         "the iphone's screen is poor"

2014-01-04         "I will always use Apple products"

2014-01-03         "iphone is overpriced, but I love it"

2014-01-03         "Siri is very sluggish"

2014-01-03         "iphone's maps app is poor compared to Android"

我想要一个循环/函数来按创建日期合并推文 结果是这样的

Created_Date       Tweet

2014-01-04         "the iphone is magnificent", "the iphone's screen is poor",              "I will always use Apple products"

2014-01-03         "iphone is overpriced, but I love it", "Siri is very sluggish", "iphone's maps app is poor compared to Android"

这是我的数据

 dat <-   structure(list(Created_Date = structure(c(1388793600, 1388793600, 
    1388793600, 1388707200, 1388707200, 1388707200), class = c("POSIXct", 
    "POSIXt"), tzone = "UTC"), Tweet = c("the iphone is magnificent", 
    "the iphone's screen is poor", "I will always use Apple products", 
    "iphone is overpriced, but I love it", "Siri is very sluggish", 
    "iphone's maps app is poor compared to Android")), .Names = c("Created_Date", 
    "Tweet"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
    -6L))

---

dat只是一个使用循环的简单实现。可能不是最快的解决方案，但很容易理解

# construction of a sample data.frame
text = c("Some random text.", 
         "Yet another line.",
         "Will this ever stop.",
         "This may be the last one.",
         "It was not the last.")
date = c("9-11-2017",
         "11-11-2017",
         "10-11-2017",
         "11-11-2017",
         "10-11-2017")
tweet = data.frame(text, date)

# array with dates in the data.frame
dates = levels(tweet$date)

# initialise results with empty strings
resultString = rep.int("", length(dates)) 

for(i in 1:length(dates)) # loop over different dates
{
    for(j in 1:length(tweet$text)) # loop over tweets
    {
        if (tweet$date[j] == dates[i]) # concatenate to resultString if dates match
        {
            resultString[i] = paste0(resultString[i], tweet$text[j])
        }
    }
}

# combine concatenated strings with dates in new data.frame
result = data.frame(date=dates, tweetsByDate=resultString)
result

# output:
# date                               tweetsByDate
# 1 10-11-2017   Will this ever stop.It was not the last.
# 2 11-11-2017 Yet another line.This may be the last one.
# 3  9-11-2017                          Some random text.

使用data.table的示例
如果您使用的是语料库，那么您可以使用
group
参数来
term\u counts
或
term\u matrix
按日期进行聚合（求和）

在您的情况下，如果您对计算肯定、否定和中性单词的数量感兴趣，可以首先创建一个“词干分析器”，将单词映射到以下类别：

library(corpus)
# map terms in the AFINN dictionary to Positive/Negative; others to Neutral
stem_sent <- new_stemmer(sentiment_afinn$term,
                         ifelse(sentiment_afinn$score > 0, "Positive", "Negative"),
                         default = "Neutral")

或者获取计数矩阵：

term_matrix(dat$Tweet, group = dat$Created_Date, stemmer = stem_sent)
## 2 x 3 sparse Matrix of class "dgCMatrix"
##            Negative Neutral Positive
## 2014-01-03        2      17        1
## 2014-01-04        1      14        .

请提供一些您想做的事情，这样我们将更好地了解您的问题，并能够为您提供更多帮助。嗨，帕特里克，非常感谢您抽出时间帮助我提出问题。这正是我想做的。我从一个国家的多个新闻组中提取了“很多”帖子，并试图先按新闻组对它们进行分组，然后再进行汇总。我这样做的原因是，我希望考察一下围绕事件（选举）的情绪。我想用syuzhet来获得情感上的细微差别Patrick。我刚刚读到了您对其他人的另一个回复，其中显示了您如何创建一个循环来通过实例处理“stopwords”。这将是一个巨大的帮助，因为我通过了数百万的职位。谢谢你
aggregate(ta$Tweet,by=list(ta$Created_Date),FUN=function(X)paste(X, collapse = ","))

library(corpus)
# map terms in the AFINN dictionary to Positive/Negative; others to Neutral
stem_sent <- new_stemmer(sentiment_afinn$term,
                         ifelse(sentiment_afinn$score > 0, "Positive", "Negative"),
                         default = "Neutral")

term_counts(dat$Tweet, group = dat$Created_Date, stemmer = stem_sent)
##   group      term     count
## 1 2014-01-03 Negative     2 
## 2 2014-01-04 Negative     1
## 3 2014-01-03 Neutral     17
## 4 2014-01-04 Neutral     14
## 5 2014-01-03 Positive     1

term_matrix(dat$Tweet, group = dat$Created_Date, stemmer = stem_sent)
## 2 x 3 sparse Matrix of class "dgCMatrix"
##            Negative Neutral Positive
## 2014-01-03        2      17        1
## 2014-01-04        1      14        .