R语言使用tf-idf描述NASA元数据的文本和关键字

NASA有32,000多个数据集,有关NASA数据集的元数据  可以JSON格式在线获得。

由Kaizong Ye,Liao Bao撰写

我们使用tf-idf在描述字段中找到重要的单词,并将其与关键字联系起来。

获取和整理NASA元数据

让我们下载32,000多个NASA数据集的元数据。


library(jsonlite)
library(dplyr)
library(tidyr)
metadata <- fromJSON("data.json")
names(metadata$dataset)
##  [1] "_id"                "@type"              "accessLevel"        "accrualPeriodicity"
##  [5] "bureauCode"         "contactPoint"       "description"        "distribution"      
##  [9] "identifier"         "issued"             "keyword"            "landingPage"       
## [13] "language"           "modified"           "programCode"        "publisher"         
## [17] "spatial"            "temporal"           "theme"              "title"             
## [21] "license"            "isPartOf"           "references"         "rights"            
## [25] "describedBy"
nasadesc <- data_frame(id = metadata$dataset$`_id`$`$oid`, desc = metadata$dataset$description)
nasadesc
## # A tibble: 32,089 x 2
##                          id
##                       <chr>
## 1  55942a57c63a7fe59b495a77
## 2  55942a57c63a7fe59b495a78
## 3  55942a58c63a7fe59b495a79
## 4  55942a58c63a7fe59b495a7a
## 5  55942a58c63a7fe59b495a7b
## 6  55942a58c63a7fe59b495a7c
## 7  55942a58c63a7fe59b495a7d
## 8  55942a58c63a7fe59b495a7e
## 9  55942a58c63a7fe59b495a7f
## 10 55942a58c63a7fe59b495a80
## # ... with 32,079 more rows, and 1 more variables: desc <chr>

## # A tibble: 32,089 x 2
##                          id
##                       <chr>
## 1  55942a57c63a7fe59b495a77
## 2  55942a57c63a7fe59b495a78
## 3  55942a58c63a7fe59b495a79
## 4  55942a58c63a7fe59b495a7a
## 5  55942a58c63a7fe59b495a7b
## 6  55942a58c63a7fe59b495a7c
## 7  55942a58c63a7fe59b495a7d
## 8  55942a58c63a7fe59b495a7e
## 9  55942a58c63a7fe59b495a7f
## 10 55942a58c63a7fe59b495a80
## # ... with 32,079 more rows, and 1 more variables: desc <chr>

让我们输出其中的一部分。


nasadesc %>% select(desc) %>% sample_n(5)
## # A tibble: 5 x 1
##                                                                                                                                                      desc
##                                                                                                                                                     <chr>
## 1  A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL P
## 2  ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The
## 3                                                                                                              Crew lock bag. Polygons: 405 Vertices: 514
## 4  JEM Engineering proved the technical feasibility of the FlexScan array?a very low-cost, highly-efficient, wideband phased array antenna?in Phase I, an
## 5 MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the\nTerra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit aro

这是关键词。

## # A tibble: 126,814 x 2
##                          id       keyword
##                       <chr>         <chr>
## 1  55942a57c63a7fe59b495a77 EARTH SCIENCE
## 2  55942a57c63a7fe59b495a77   HYDROSPHERE
## 3  55942a57c63a7fe59b495a77 SURFACE WATER
## 4  55942a57c63a7fe59b495a78 EARTH SCIENCE
## 5  55942a57c63a7fe59b495a78   HYDROSPHERE
## 6  55942a57c63a7fe59b495a78 SURFACE WATER
## 7  55942a58c63a7fe59b495a79 EARTH SCIENCE
## 8  55942a58c63a7fe59b495a79   HYDROSPHERE
## 9  55942a58c63a7fe59b495a79 SURFACE WATER
## 10 55942a58c63a7fe59b495a7a EARTH SCIENCE
## # ... with 126,804 more rows

最常见的关键字是什么?


自适应网页宽度的 Bilibili 视频

视频

文本挖掘:主题模型(LDA)及R语言实现分析游记数据

探索见解

去bilibili观看

探索更多视频


## # A tibble: 1,774 x 2
##                    keyword     n
##                      <chr> <int>
## 1            EARTH SCIENCE 14362
## 2                  Project  7452
## 3               ATMOSPHERE  7321
## 4              Ocean Color  7268
## 5             Ocean Optics  7268
## 6                   Oceans  7268
## 7                completed  6452
## 8  ATMOSPHERIC WATER VAPOR  3142
## 9                   OCEANS  2765
## 10            LAND SURFACE  2720
## # ... with 1,764 more rows

看起来“已完成项目”对于某些目的来说可能不是有用的关键字,我们可能希望将所有这些都更改为小写或大写,以消除如“ OCEANS”和“ Oceans”之类的重复项。

计算文字的tf-idf

什么是tf-idf?评估文档中单词的重要性的一种方法可能是其  术语频率  (tf),即单词在文档中出现的频率。但是,一些经常出现的单词并不重要。在英语中,这些词可能是“ the”,“ is”,“ of”等词。另一种方法是查看术语的  逆文本频率指数 (idf),这会降低常用单词的权重,而增加在文档集中很少使用的单词的权重。


## # A tibble: 2,728,224 x 3
##                          id  word     n
##                       <chr> <chr> <int>
## 1  55942a88c63a7fe59b498280   amp   679
## 2  55942a88c63a7fe59b498280  nbsp   655
## 3  55942a8ec63a7fe59b4986ef    gt   330
## 4  55942a8ec63a7fe59b4986ef    lt   330
## 5  55942a8ec63a7fe59b4986ef     p   327
## 6  55942a8ec63a7fe59b4986ef   the   231
## 7  55942a86c63a7fe59b49803b   amp   208
## 8  55942a86c63a7fe59b49803b  nbsp   204
## 9  56cf5b00a759fdadc44e564a   the   201
## 10 55942a86c63a7fe59b4980a2    gt   191
## # ... with 2,728,214 more rows

这些是NASA字段中最常见的“单词”,是词频最高的单词。让我们看一下第一个数据集,例如:

## # A tibble: 1 x 1
##                                                                                                                                                     desc
##                                                                                                                                                    <chr>
## 1 The objective of the Variable Oxygen Regulator Element is to develop an oxygen-rated, contaminant-tolerant oxygen regulator to control suit p

tf-idf算法应该减少所有这些的权重,因为它们很常见,但是我们可以根据需要通过停用词将其删除。现在,让我们为描述字段中的所有单词计算tf-idf。

## # A tibble: 2,728,224 x 6
##                          id  word     n         tf       idf      tf_idf
##                       <chr> <chr> <int>      <dbl>     <dbl>       <dbl>
## 1  55942a88c63a7fe59b498280   amp   679 0.35661765 3.1810813 1.134429711
## 2  55942a88c63a7fe59b498280  nbsp   655 0.34401261 4.2066578 1.447143322
## 3  55942a8ec63a7fe59b4986ef    gt   330 0.05722213 3.2263517 0.184618705
## 4  55942a8ec63a7fe59b4986ef    lt   330 0.05722213 3.2903671 0.188281801
## 5  55942a8ec63a7fe59b4986ef     p   327 0.05670192 3.3741126 0.191318680
## 6  55942a8ec63a7fe59b4986ef   the   231 0.04005549 0.1485621 0.005950728
## 7  55942a86c63a7fe59b49803b   amp   208 0.32911392 3.1810813 1.046938133
## 8  55942a86c63a7fe59b49803b  nbsp   204 0.32278481 4.2066578 1.357845252
## 9  56cf5b00a759fdadc44e564a   the   201 0.06962245 0.1485621 0.010343258
## 10 55942a86c63a7fe59b4980a2    gt   191 0.12290862 3.2263517 0.396546449
## # ... with 2,728,214 more rows

添加的列是tf,idf,这两个数量相乘在一起是tf-idf。NASA描述字段中最高的tf-idf词是什么?


R语言文本挖掘NASA数据网络分析,tf-idf和主题建模

阅读文章



## # A tibble: 2,728,224 x 6
##                          id                                          word     n    tf       idf
##                       <chr>                                         <chr> <int> <dbl>     <dbl>
## 1  55942a7cc63a7fe59b49774a                                           rdr     1     1 10.376269
## 2  55942ac9c63a7fe59b49b688 palsar_radiometric_terrain_corrected_high_res     1     1 10.376269
## 3  55942ac9c63a7fe59b49b689  palsar_radiometric_terrain_corrected_low_res     1     1 10.376269
## 4  55942a7bc63a7fe59b4976ca                                          lgrs     1     1  8.766831
## 5  55942a7bc63a7fe59b4976d2                                          lgrs     1     1  8.766831
## 6  55942a7bc63a7fe59b4976e3                                          lgrs     1     1  8.766831
## 7  55942ad8c63a7fe59b49cf6c                      template_proddescription     1     1  8.296827
## 8  55942ad8c63a7fe59b49cf6d                      template_proddescription     1     1  8.296827
## 9  55942ad8c63a7fe59b49cf6e                      template_proddescription     1     1  8.296827
## 10 55942ad8c63a7fe59b49cf6f                      template_proddescription     1     1  8.296827
##       tf_idf
##        <dbl>
## 1  10.376269
## 2  10.376269
## 3  10.376269
## 4   8.766831
## 5   8.766831
## 6   8.766831
## 7   8.296827
## 8   8.296827
## 9   8.296827
## 10  8.296827
## # ... with 2,728,214 more rows

因此,这些是用tf-idf衡量的描述字段中最“重要”的词,这意味着它们很常见,但不太常用。

## # A tibble: 1 x 1
##    desc
##   <chr>
## 1   RDR

tf-idf算法认为这非常重要的词。

关键字和描述

因此,现在我们知道描述中的哪个词具有较高的tf-idf,并且在关键字中也有这些描述的标签。

## # A tibble: 11,013,838 x 7
##                          id  word     n         tf      idf    tf_idf              keyword
##                       <chr> <chr> <int>      <dbl>    <dbl>     <dbl>                <chr>
## 1  55942a88c63a7fe59b498280   amp   679 0.35661765 3.181081 1.1344297              ELEMENT
## 2  55942a88c63a7fe59b498280   amp   679 0.35661765 3.181081 1.1344297 JOHNSON SPACE CENTER
## 3  55942a88c63a7fe59b498280   amp   679 0.35661765 3.181081 1.1344297                  VOR
## 4  55942a88c63a7fe59b498280   amp   679 0.35661765 3.181081 1.1344297               ACTIVE
## 5  55942a88c63a7fe59b498280  nbsp   655 0.34401261 4.206658 1.4471433              ELEMENT
## 6  55942a88c63a7fe59b498280  nbsp   655 0.34401261 4.206658 1.4471433 JOHNSON SPACE CENTER
## 7  55942a88c63a7fe59b498280  nbsp   655 0.34401261 4.206658 1.4471433                  VOR
## 8  55942a88c63a7fe59b498280  nbsp   655 0.34401261 4.206658 1.4471433               ACTIVE
## 9  55942a8ec63a7fe59b4986ef    gt   330 0.05722213 3.226352 0.1846187 JOHNSON SPACE CENTER
## 10 55942a8ec63a7fe59b4986ef    gt   330 0.05722213 3.226352 0.1846187              PROJECT
## # ... with 11,013,828 more rows

可视化结果

让我们来看几个示例关键字中最重要的单词。

## # A tibble: 122 x 7
##                          id      word     n        tf      idf   tf_idf    keyword
##                       <chr>    <fctr> <int>     <dbl>    <dbl>    <dbl>      <chr>
## 1  55942a60c63a7fe59b49612f estimates     1 0.5000000 3.172863 1.586432     CLOUDS
## 2  55942a76c63a7fe59b49728d      ncdc     1 0.1666667 7.603680 1.267280     CLOUDS
## 3  55942a60c63a7fe59b49612f     cloud     1 0.5000000 2.464212 1.232106     CLOUDS
## 4  55942a5ac63a7fe59b495bd8      fife     1 0.2000000 5.910360 1.182072     CLOUDS
## 5  55942a5cc63a7fe59b495deb allometry     1 0.1428571 7.891362 1.127337 VEGETATION
## 6  55942a5dc63a7fe59b495ede       tgb     3 0.1875000 5.945452 1.114772 VEGETATION
## 7  55942a5ac63a7fe59b495bd8      tovs     1 0.2000000 5.524238 1.104848     CLOUDS
## 8  55942a5ac63a7fe59b495bd8  received     1 0.2000000 5.332843 1.066569     CLOUDS
## 9  55942a5cc63a7fe59b495dfd       sap     1 0.1250000 8.430358 1.053795 VEGETATION
## 10 55942a60c63a7fe59b496131  abstract     1 0.3333333 3.118561 1.039520     CLOUDS
## # ... with 112 more rows
## # A tibble: 1 x 1
##              desc
##             <chr>
## 1 Cloud estimates

tf-idf算法在仅2个字长的描述中无法很好地工作,或者它将对这些字加权过重。这是不合适的。


可下载资源

关于作者

Kaizong Ye拓端研究室(TRL)的研究员。

本文借鉴了作者最近为《R语言数据分析挖掘必知必会 》课堂做的准备。

​非常感谢您阅读本文,如需帮助请联系我们!

 
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