Elasticsearch：如何实现对 emoji 表情符号进行搜索

转摘自：https://elasticstack.blog.csdn.net/article/details/114261636

Elasticsearch 是一个应用非常广泛的搜索引擎。它可以对文字进行分词，从而实现全文搜索。在实际的使用中，我们会发现有一些文字中包含一些表情符号，比如笑脸，动物等等，那么我们该如何对这些表情符号来进行搜索呢？
     => , light skin tone, skin tone, type 1–2
     => , medium-light skin tone, skin tone, type 3
     => , medium skin tone, skin tone, type 4
     => , medium-dark skin tone, skin tone, type 5
     => , dark skin tone, skin tone, type 6
    ♪ => ♪, eighth, music, note
    ♭ => ♭, bemolle, flat, music, note
    ♯ => ♯, dièse, diesis, music, note, sharp
     => , face, grin, grinning face
     => , face, grinning face with big eyes, mouth, open, smile
     => , eye, face, grinning face with smiling eyes, mouth, open, smile
     => , beaming face with smiling eyes, eye, face, grin, smile
     => , face, grinning squinting face, laugh, mouth, satisfied, smile
     => , cold, face, grinning face with sweat, open, smile, sweat
     => , face, floor, laugh, rofl, rolling, rolling on the floor laughing, rotfl
     => , face, face with tears of joy, joy, laugh, tear
     => , face, slightly smiling face, smile
     => , face, upside-down
     => , face, wink, winking face
     => , tiger
     => , leopard
     => , face, horse
     => , equestrian, horse, racehorse, racing
     => , face, unicorn
     => , stripe, zebra
     => , deer
在上面，我们可以看到各种各样的 emoji 符号。比如我们想搜索 grin，那么它就把含有  emoji 符号的文档也找出来。在今天的文章中，我们来展示如何实现对 emoji 符号的进行搜索。
安装
如果你还没有对 Elasticsearch 及 Kibana 进行安装的话，请参阅之前的文章 “Elastic：菜鸟上手指南” 进行安装。 另外，我们必须安装 ICU analyzer。关于 ICU analyzer 的安装，请参阅之前的文章 “Elasticsearch：ICU 分词器介绍”。我们在 Elasticsearch 的安装根目录中，打入如下的命令：
./bin/elasticsearch-plugin install analysis-icu
等安装好后，我们需要重新启动 Elasticsearch 让它起作用。运行：
./bin/elasticsearch-plugin list
上面的命令显示：
    $ ./bin/elasticsearch-plugin install analysis-icu
    -> Installing analysis-icu
    -> Downloading analysis-icu from elastic
    [=================================================] 100%
    -> Installed analysis-icu
    $ ./bin/elasticsearch-plugin list
    analysis-icu
安装完 ICU analyzer 后，我们必须重新启动 Elasticsearch。
搜索 emoji 符号
我们先做一个简单的实验：
    GET /_analyze
    {
      "tokenizer": "icu_tokenizer",
      "text": "I live in  and I'm ‍"
    }
上面使用 icu_tokenizer 来对 “I live in   and I'm ‍” 进行分词。 ‍ 表情符号非常独特，因为它是更经典的  和  表情符号的组合。 中国的国旗也很特别，它是  和  的组合。 因此，我们不仅在谈论正确地分割 Unicode 代码点，而且在这里真正地了解了表情符号。
上面的请求的返回结果为：
    {
      "tokens" : [
        {
          "token" : "I",
          "start_offset" : 0,
          "end_offset" : 1,
          "type" : "<ALPHANUM>",
          "position" : 0
        },
        {
          "token" : "live",
          "start_offset" : 2,
          "end_offset" : 6,
          "type" : "<ALPHANUM>",
          "position" : 1
        },
        {
          "token" : "in",
          "start_offset" : 7,
          "end_offset" : 9,
          "type" : "<ALPHANUM>",
          "position" : 2
        },
        {
          "token" : """""",
          "start_offset" : 10,
          "end_offset" : 14,
          "type" : "<EMOJI>",
          "position" : 3
        },
        {
          "token" : "and",
          "start_offset" : 16,
          "end_offset" : 19,
          "type" : "<ALPHANUM>",
          "position" : 4
        },
        {
          "token" : "I'm",
          "start_offset" : 20,
          "end_offset" : 23,
          "type" : "<ALPHANUM>",
          "position" : 5
        },
        {
          "token" : """‍""",
          "start_offset" : 24,
          "end_offset" : 29,
          "type" : "<EMOJI>",
          "position" : 6
        }
      ]
    }
显然 emoji 的符号被正确地分词，并能被搜索。
在实际的使用中，我们可能并不限限于对这些 emoji 的符号的搜索。比如我们想对如下的文档进行搜索：
    PUT emoji-capable/_doc/1
    {
      "content": "I like "
    }
上面的文档中含有一个 ，也就是老虎。针对上面的文档，我们想搜索 tiger 的时候，也能正确地搜索到文档，那么我们该如何去做呢？
在 github 上面，有一个项目叫做 https://github.com/jolicode/emoji-search/。在它的项目中，有一个目录 https://github.com/jolicode/emoji-search/tree/master/synonyms。这里其实就是同义词的目录。我们现在下载其中的一个文件 https://github.com/jolicode/emoji-search/blob/master/synonyms/cldr-emoji-annotation-synonyms-en.txt 到 Elasticsearch 的本地安装目录：
    config
    ├── analysis
    │   ├── cldr-emoji-annotation-synonyms-en.txt
    │   └── emoticons.txt
    ├── elasticsearch.yml
    ...
在我的电脑上：
    $ pwd
    /Users/liuxg/elastic1/elasticsearch-7.11.0/config
    $ tree -L 3
    .
    ├── analysis
    │   └── cldr-emoji-annotation-synonyms-en.txt
    ├── elasticsearch.keystore
    ├── elasticsearch.yml
    ├── jvm.options
    ├── jvm.options.d
    ├── log4j2.properties
    ├── role_mapping.yml
    ├── roles.yml
    ├── users
    └── users_roles
在上面的 cldr-emoji-annotation-synonyms-en.txt 的文件中，它包含了常见 emoji 的符号的同义词。比如：
     => , face, grin, grinning face
     => , face, grinning face with big eyes, mouth, open, smile
     => , eye, face, grinning face with smiling eyes, mouth, open, smile
     => , beaming face with smiling eyes, eye, face, grin, smile
     => , face, grinning squinting face, laugh, mouth, satisfied, smile
     => , cold, face, grinning face with sweat, open, smile, sweat
    ....
为此，我们来进行如下的实验：
    PUT /emoji-capable
    {
      "settings": {
        "analysis": {
          "filter": {
            "english_emoji": {
              "type": "synonym",
              "synonyms_path": "analysis/cldr-emoji-annotation-synonyms-en.txt"
            }
          },
          "analyzer": {
            "english_with_emoji": {
              "tokenizer": "icu_tokenizer",
              "filter": [
                "english_emoji"
              ]
            }
          }
        }
      },
      "mappings": {
        "properties": {
          "content": {
            "type": "text",
            "analyzer": "english_with_emoji"
          }
        }
      }
    }
在上面，我们定义了 english_with_emoji 分词器，同时我们在定义 content 字段时也使用相同的分词器 english_with_emoji。我们使用 _analyze API 来进行如下的使用：
    GET emoji-capable/_analyze
    {
      "analyzer": "english_with_emoji",
      "text": "I like "
    }
上面的命令返回：
    {
      "tokens" : [
        {
          "token" : "I",
          "start_offset" : 0,
          "end_offset" : 1,
          "type" : "<ALPHANUM>",
          "position" : 0
        },
        {
          "token" : "like",
          "start_offset" : 2,
          "end_offset" : 6,
          "type" : "<ALPHANUM>",
          "position" : 1
        },
        {
          "token" : """""",
          "start_offset" : 7,
          "end_offset" : 9,
          "type" : "SYNONYM",
          "position" : 2
        },
        {
          "token" : "tiger",
          "start_offset" : 7,
          "end_offset" : 9,
          "type" : "SYNONYM",
          "position" : 2
        }
      ]
    }
显然它除了返回 ， 也同时返回了 tiger 这样的 token。也就是说我们可以同时搜索这两种，都可以搜索到这个文档。同样地：
    GET emoji-capable/_analyze
    {
      "analyzer": "english_with_emoji",
      "text": " means happy"
    }
它返回：
    {
      "tokens" : [
        {
          "token" : """""",
          "start_offset" : 0,
          "end_offset" : 2,
          "type" : "SYNONYM",
          "position" : 0
        },
        {
          "token" : "face",
          "start_offset" : 0,
          "end_offset" : 2,
          "type" : "SYNONYM",
          "position" : 0
        },
        {
          "token" : "grin",
          "start_offset" : 0,
          "end_offset" : 2,
          "type" : "SYNONYM",
          "position" : 0
        },
        {
          "token" : "grinning",
          "start_offset" : 0,
          "end_offset" : 2,
          "type" : "SYNONYM",
          "position" : 0
        },
        {
          "token" : "means",
          "start_offset" : 3,
          "end_offset" : 8,
          "type" : "<ALPHANUM>",
          "position" : 1
        },
        {
          "token" : "face",
          "start_offset" : 3,
          "end_offset" : 8,
          "type" : "SYNONYM",
          "position" : 1
        },
        {
          "token" : "happy",
          "start_offset" : 9,
          "end_offset" : 14,
          "type" : "<ALPHANUM>",
          "position" : 2
        }
      ]
    }
它表明，如果我们搜索 face, grinning，grin，该文档也会被正确地返回。
现在，我们输入如下的两个文档：
    PUT emoji-capable/_doc/1
    {
      "content": "I like "
    }
    PUT emoji-capable/_doc/2
    {
      "content": " means happy"
    }
我们对文档进行如下的搜索：
    GET emoji-capable/_search
    {
      "query": {
        "match": {
          "content": ""
        }
      }
    }
或：
    GET emoji-capable/_search
    {
      "query": {
        "match": {
          "content": "tiger"
        }
      }
    }
他们都将返回第一个文档：
    {
      "took" : 2,
      "timed_out" : false,
      "_shards" : {
        "total" : 1,
        "successful" : 1,
        "skipped" : 0,
        "failed" : 0
      },
      "hits" : {
        "total" : {
          "value" : 1,
          "relation" : "eq"
        },
        "max_score" : 0.8514803,
        "hits" : [
          {
            "_index" : "emoji-capable",
            "_type" : "_doc",
            "_id" : "1",
            "_score" : 0.8514803,
            "_source" : {
              "content" : """I like """
            }
          }
        ]
      }
    }
通用地，我们进行如下的搜索：
    GET emoji-capable/_search
    {
      "query": {
        "match": {
          "content": ""
        }
      }
    }
或者：
    GET emoji-capable/_search
    {
      "query": {
        "match": {
          "content": "grin"
        }
      }
    }
它们都将返回第二个文档：
    {
      "took" : 1,
      "timed_out" : false,
      "_shards" : {
        "total" : 1,
        "successful" : 1,
        "skipped" : 0,
        "failed" : 0
      },
      "hits" : {
        "total" : {
          "value" : 1,
          "relation" : "eq"
        },
        "max_score" : 0.8514803,
        "hits" : [
          {
            "_index" : "emoji-capable",
            "_type" : "_doc",
            "_id" : "2",
            "_score" : 0.8514803,
            "_source" : {
              "content" : """ means happy"""
            }
          }
        ]
      }
    }