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从列中提取单词并计算频率

[英]Extract words from a column and count frequency

Does anyone know if there's an efficient way to extract all the words from a single column and count the frequency of each word in SQL Server?有谁知道是否有一种有效的方法可以从单个列中提取所有单词并计算 SQL 服务器中每个单词的频率? I only have read-only access to my database so I can't create a self-defined function to do this.我只有对我的数据库的只读访问权限,所以我无法创建一个自定义的 function 来执行此操作。

Here's a reproducible example:这是一个可重现的例子:

CREATE TABLE words 
(
    id INT PRIMARY KEY,
    text_column VARCHAR(1000)
);

INSERT INTO words (id, text_column)
VALUES
    (1, 'SQL Server is a popular database management system'),
    (2, 'It is widely used for data storage and retrieval'),
    (3, 'SQL Server is a powerful tool for data analysis');

I have found this code but it's not working correctly, and I think it's too complicated to understand:我找到了这段代码,但它不能正常工作,而且我认为它太复杂了,难以理解:

WITH E1(N) AS 
(
    SELECT 1 
    FROM (VALUES
           (1),(1),(1),(1),(1),(1),(1),(1),(1),(1)
         ) t(N)
),
E2(N) AS (SELECT 1 FROM E1 a CROSS JOIN E1 b),
E4(N) AS (SELECT 1 FROM E2 a CROSS JOIN E2 b)
SELECT
    LOWER(x.Item) AS [Word],
    COUNT(*) AS [Counts]
FROM 
    (SELECT * FROM words) a
CROSS APPLY 
    (SELECT 
         ItemNumber = ROW_NUMBER() OVER(ORDER BY l.N1),
         Item       = LTRIM(RTRIM(SUBSTRING(a.text_column, l.N1, l.L1)))
     FROM 
         (SELECT 
              s.N1,
              L1 = ISNULL(NULLIF(CHARINDEX(' ',a.text_column,s.N1),0)-s.N1,4000)
          FROM
              (SELECT 1 
               UNION ALL
               SELECT t.N+1 
               FROM
                   (SELECT TOP (ISNULL(DATALENGTH(a.text_column)/2,0))
                        ROW_NUMBER() OVER (ORDER BY (SELECT NULL))
                    FROM E4) t(N)
               WHERE SUBSTRING(a.text_column ,t.N,1) = ' '
              ) s(N1)
        ) l(N1, L1)
) x
WHERE 
    x.item <> '' 
    AND x.Item NOT IN ('0o', '0s', '3a', '3b', '3d', '6b', '6o', 'a', 'a1', 'a2', 'a3', 'a4', 'ab', 'able', 'about', 'above', 'abst', 'ac', 'accordance', 'according', 'accordingly', 'across', 'act', 'actually', 'ad', 'added', 'adj', 'ae', 'af', 'affected', 'affecting', 'affects', 'after', 'afterwards', 'ag', 'again', 'against', 'ah', 'ain', 'ain''t', 'aj', 'al', 'all', 'allow', 'allows', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst', 'amount', 'an', 'and', 'announce', 'another', 'any', 'anybody', 'anyhow', 'anymore', 'anyone', 'anything', 'anyway', 'anyways', 'anywhere', 'ao', 'ap', 'apart', 'apparently', 'appear', 'appreciate', 'appropriate', 'approximately', 'ar', 'are', 'aren', 'arent', 'aren''t', 'arise', 'around', 'as', 'a''s', 'aside', 'ask', 'asking', 'associated', 'at', 'au', 'auth', 'av', 'available', 'aw', 'away', 'awfully', 'ax', 'ay', 'az', 'b', 'b1', 'b2', 'b3', 'ba', 'back', 'bc', 'bd', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'begin', 'beginning', 'beginnings', 'begins', 'behind', 'being', 'believe', 'below', 'beside', 'besides', 'best', 'better', 'between', 'beyond', 'bi', 'bill', 'biol', 'bj', 'bk', 'bl', 'bn', 'both', 'bottom', 'bp', 'br', 'brief', 'briefly', 'bs', 'bt', 'bu', 'but', 'bx', 'by', 'c', 'c1', 'c2', 'c3', 'ca', 'call', 'came', 'can', 'cannot', 'cant', 'can''t', 'cause', 'causes', 'cc', 'cd', 'ce', 'certain', 'certainly', 'cf', 'cg', 'ch', 'changes', 'ci', 'cit', 'cj', 'cl', 'clearly', 'cm', 'c''mon', 'cn', 'co', 'com', 'come', 'comes', 'con', 'concerning', 'consequently', 'consider', 'considering', 'contain', 'containing', 'contains', 'corresponding', 'could', 'couldn', 'couldnt', 'couldn''t', 'course', 'cp', 'cq', 'cr', 'cry', 'cs', 'c''s', 'ct', 'cu', 'currently', 'cv', 'cx', 'cy', 'cz', 'd', 'd2', 'da', 'date', 'dc', 'dd', 'de', 'definitely', 'describe', 'described', 'despite', 'detail', 'df', 'di', 'did', 'didn', 'didn''t', 'different', 'dj', 'dk', 'dl', 'do', 'does', 'doesn', 'doesn''t', 'doing', 'don', 'done', 'don''t', 'down', 'downwards', 'dp', 'dr', 'ds', 'dt', 'du', 'due', 'during', 'dx', 'dy', 'e', 'e2', 'e3', 'ea', 'each', 'ec', 'ed', 'edu', 'ee', 'ef', 'effect', 'eg', 'ei', 'eight', 'eighty', 'either', 'ej', 'el', 'eleven', 'else', 'elsewhere', 'em', 'empty', 'en', 'end', 'ending', 'enough', 'entirely', 'eo', 'ep', 'eq', 'er', 'es', 'especially', 'est', 'et', 'et-al', 'etc', 'eu', 'ev', 'even', 'ever', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'ex', 'exactly', 'example', 'except', 'ey', 'f', 'f2', 'fa', 'far', 'fc', 'few', 'ff', 'fi', 'fifteen', 'fifth', 'fify', 'fill', 'find', 'fire', 'first', 'five', 'fix', 'fj', 'fl', 'fn', 'fo', 'followed', 'following', 'follows', 'for', 'former', 'formerly', 'forth', 'forty', 'found', 'four', 'fr', 'from', 'front', 'fs', 'ft', 'fu', 'full', 'further', 'furthermore', 'fy', 'g', 'ga', 'gave', 'ge', 'get', 'gets', 'getting', 'gi', 'give', 'given', 'gives', 'giving', 'gj', 'gl', 'go', 'goes', 'going', 'gone', 'got', 'gotten', 'gr', 'greetings', 'gs', 'gy', 'h', 'h2', 'h3', 'had', 'hadn', 'hadn''t', 'happens', 'hardly', 'has', 'hasn', 'hasnt', 'hasn''t', 'have', 'haven', 'haven''t', 'having', 'he', 'hed', 'he''d', 'he''ll', 'hello', 'help', 'hence', 'her', 'here', 'hereafter', 'hereby', 'herein', 'heres', 'here''s', 'hereupon', 'hers', 'herself', 'hes', 'he''s', 'hh', 'hi', 'hid', 'him', 'himself', 'his', 'hither', 'hj', 'ho', 'home', 'hopefully', 'how', 'howbeit', 'however', 'how''s', 'hr', 'hs', 'http', 'hu', 'hundred', 'hy', 'i', 'i2', 'i3', 'i4', 'i6', 'i7', 'i8', 'ia', 'ib', 'ibid', 'ic', 'id', 'i''d', 'ie', 'if', 'ig', 'ignored', 'ih', 'ii', 'ij', 'il', 'i''ll', 'im', 'i''m', 'immediate', 'immediately', 'importance', 'important', 'in', 'inasmuch', 'inc', 'indeed', 'index', 'indicate', 'indicated', 'indicates', 'information', 'inner', 'insofar', 'instead', 'interest', 'into', 'invention', 'inward', 'io', 'ip', 'iq', 'ir', 'is', 'isn', 'isn''t', 'it', 'itd', 'it''d', 'it''ll', 'its', 'it''s', 'itself', 'iv', 'i''ve', 'ix', 'iy', 'iz', 'j', 'jj', 'jr', 'js', 'jt', 'ju', 'just', 'k', 'ke', 'keep', 'keeps', 'kept', 'kg', 'kj', 'km', 'know', 'known', 'knows', 'ko', 'l', 'l2', 'la', 'largely', 'last', 'lately', 'later', 'latter', 'latterly', 'lb', 'lc', 'le', 'least', 'les', 'less', 'lest', 'let', 'lets', 'let''s', 'lf', 'like', 'liked', 'likely', 'line', 'little', 'lj', 'll', 'll', 'ln', 'lo', 'look', 'looking', 'looks', 'los', 'lr', 'ls', 'lt', 'ltd', 'm', 'm2', 'ma', 'made', 'mainly', 'make', 'makes', 'many', 'may', 'maybe', 'me', 'mean', 'means', 'meantime', 'meanwhile', 'merely', 'mg', 'might', 'mightn', 'mightn''t', 'mill', 'million', 'mine', 'miss', 'ml', 'mn', 'mo', 'more', 'moreover', 'most', 'mostly', 'move', 'mr', 'mrs', 'ms', 'mt', 'mu', 'much', 'mug', 'must', 'mustn', 'mustn''t', 'my', 'myself', 'n', 'n2', 'na', 'name', 'namely', 'nay', 'nc', 'nd', 'ne', 'near', 'nearly', 'necessarily', 'necessary', 'need', 'needn', 'needn''t', 'needs', 'neither', 'never', 'nevertheless', 'new', 'next', 'ng', 'ni', 'nine', 'ninety', 'nj', 'nl', 'nn', 'no', 'nobody', 'non', 'none', 'nonetheless', 'noone', 'nor', 'normally', 'nos', 'not', 'noted', 'nothing', 'novel', 'now', 'nowhere', 'nr', 'ns', 'nt', 'ny', 'o', 'oa', 'ob', 'obtain', 'obtained', 'obviously', 'oc', 'od', 'of', 'off', 'often', 'og', 'oh', 'oi', 'oj', 'ok', 'okay', 'ol', 'old', 'om', 'omitted', 'on', 'once', 'one', 'ones', 'only', 'onto', 'oo', 'op', 'oq', 'or', 'ord', 'os', 'ot', 'other', 'others', 'otherwise', 'ou', 'ought', 'our', 'ours', 'ourselves', 'out', 'outside', 'over', 'overall', 'ow', 'owing', 'own', 'ox', 'oz', 'p', 'p1', 'p2', 'p3', 'page', 'pagecount', 'pages', 'par', 'part', 'particular', 'particularly', 'pas', 'past', 'pc', 'pd', 'pe', 'per', 'perhaps', 'pf', 'ph', 'pi', 'pj', 'pk', 'pl', 'placed', 'please', 'plus', 'pm', 'pn', 'po', 'poorly', 'possible', 'possibly', 'potentially', 'pp', 'pq', 'pr', 'predominantly', 'present', 'presumably', 'previously', 'primarily', 'probably', 'promptly', 'proud', 'provides', 'ps', 'pt', 'pu', 'put', 'py', 'q', 'qj', 'qu', 'que', 'quickly', 'quite', 'qv', 'r', 'r2', 'ra', 'ran', 'rather', 'rc', 'rd', 're', 'readily', 'really', 'reasonably', 'recent', 'recently', 'ref', 'refs', 'regarding', 'regardless', 'regards', 'related', 'relatively', 'research', 'research-articl', 'respectively', 'resulted', 'resulting', 'results', 'rf', 'rh', 'ri', 'right', 'rj', 'rl', 'rm', 'rn', 'ro', 'rq', 'rr', 'rs', 'rt', 'ru', 'run', 'rv', 'ry', 's', 's2', 'sa', 'said', 'same', 'saw', 'say', 'saying', 'says', 'sc', 'sd', 'se', 'sec', 'second', 'secondly', 'section', 'see', 'seeing', 'seem', 'seemed', 'seeming', 'seems', 'seen', 'self', 'selves', 'sensible', 'sent', 'serious', 'seriously', 'seven', 'several', 'sf', 'shall', 'shan', 'shan''t', 'she', 'shed', 'she''d', 'she''ll', 'shes', 'she''s', 'should', 'shouldn', 'shouldn''t', 'should''ve', 'show', 'showed', 'shown', 'showns', 'shows', 'si', 'side', 'significant', 'significantly', 'similar', 'similarly', 'since', 'sincere', 'six', 'sixty', 'sj', 'sl', 'slightly', 'sm', 'sn', 'so', 'some', 'somebody', 'somehow', 'someone', 'somethan', 'something', 'sometime', 'sometimes', 'somewhat', 'somewhere', 'soon', 'sorry', 'sp', 'specifically', 'specified', 'specify', 'specifying', 'sq', 'sr', 'ss', 'st', 'still', 'stop', 'strongly', 'sub', 'substantially', 'successfully', 'such', 'sufficiently', 'suggest', 'sup', 'sure', 'sy', 'system', 'sz', 't', 't1', 't2', 't3', 'take', 'taken', 'taking', 'tb', 'tc', 'td', 'te', 'tell', 'ten', 'tends', 'tf', 'th', 'than', 'thank', 'thanks', 'thanx', 'that', 'that''ll', 'thats', 'that''s', 'that''ve', 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'thence', 'there', 'thereafter', 'thereby', 'thered', 'therefore', 'therein', 'there''ll', 'thereof', 'therere', 'theres', 'there''s', 'thereto', 'thereupon', 'there''ve', 'these', 'they', 'theyd', 'they''d', 'they''ll', 'theyre', 'they''re', 'they''ve', 'thickv', 'thin', 'think', 'third', 'this', 'thorough', 'thoroughly', 'those', 'thou', 'though', 'thoughh', 'thousand', 'three', 'throug', 'through', 'throughout', 'thru', 'thus', 'ti', 'til', 'tip', 'tj', 'tl', 'tm', 'tn', 'to', 'together', 'too', 'took', 'top', 'toward', 'towards', 'tp', 'tq', 'tr', 'tried', 'tries', 'truly', 'try', 'trying', 'ts', 't''s', 'tt', 'tv', 'twelve', 'twenty', 'twice', 'two', 'tx', 'u', 'u201d', 'ue', 'ui', 'uj', 'uk', 'um', 'un', 'under', 'unfortunately', 'unless', 'unlike', 'unlikely', 'until', 'unto', 'uo', 'up', 'upon', 'ups', 'ur', 'us', 'use', 'used', 'useful', 'usefully', 'usefulness', 'uses', 'using', 'usually', 'ut', 'v', 'va', 'value', 'various', 'vd', 've', 've', 'very', 'via', 'viz', 'vj', 'vo', 'vol', 'vols', 'volumtype', 'vq', 'vs', 'vt', 'vu', 'w', 'wa', 'want', 'wants', 'was', 'wasn', 'wasnt', 'wasn''t', 'way', 'we', 'wed', 'we''d', 'welcome', 'well', 'we''ll', 'well-b', 'went', 'were', 'we''re', 'weren', 'werent', 'weren''t', 'we''ve', 'what', 'whatever', 'what''ll', 'whats', 'what''s', 'when', 'whence', 'whenever', 'when''s', 'where', 'whereafter', 'whereas', 'whereby', 'wherein', 'wheres', 'where''s', 'whereupon', 'wherever', 'whether', 'which', 'while', 'whim', 'whither', 'who', 'whod', 'whoever', 'whole', 'who''ll', 'whom', 'whomever', 'whos', 'who''s', 'whose', 'why', 'why''s', 'wi', 'widely', 'will', 'willing', 'wish', 'with', 'within', 'without', 'wo', 'won', 'wonder', 'wont', 'won''t', 'words', 'world', 'would', 'wouldn', 'wouldnt', 'wouldn''t', 'www', 'x', 'x1', 'x2', 'x3', 'xf', 'xi', 'xj', 'xk', 'xl', 'xn', 'xo', 'xs', 'xt', 'xv', 'xx', 'y', 'y2', 'yes', 'yet', 'yj', 'yl', 'you', 'youd', 'you''d', 'you''ll', 'your', 'youre', 'you''re', 'yours', 'yourself', 'yourselves', 'you''ve', 'yr', 'ys', 'yt', 'z', 'zero', 'zi', 'zz')
GROUP BY x.Item
ORDER BY COUNT(*) DESC

Here's the result of the above code, as you can see it's not counting correctly:这是上面代码的结果,如您所见,它没有正确计数:

Word       Counts
server       2
sql          2
data         1
database     1
popular      1
powerful     1

Can anyone help on this?任何人都可以帮忙吗? Would be really appreciated!将不胜感激!

You can make use of String_split here, such as您可以在这里使用String_split ,例如

select value Word, Count(*) Counts
from words
cross apply String_Split(text_column, ' ')
where value not in(exclude list)
group by value
order by counts desc;

You should should the string_split function -- like this你应该 string_split function - 像这样

SELECT id, value as aword  
FROM words
CROSS APPLY STRING_SPLIT(text_column, ',');  

This will create a table with all the words by id -- to get the count do this:这将通过 id 创建一个包含所有单词的表——要获得计数,请执行以下操作:

SELECT aword, count(*) as counts
FROM (
  SELECT id, value as aword  
  FROM words
  CROSS APPLY STRING_SPLIT(text_column, ',');  
) x
GROUP BY aword

You may need to lower case the LOWER(text_column) if you want it to not matter如果您不希望它无关紧要,您可能需要将 LOWER(text_column) 小写

If you don't have access to STRING_SPLIT function, you can use weird xml trick to convert space to a word node and then shred it with nodes function:如果你无权访问 STRING_SPLIT function,你可以使用奇怪的 xml 技巧将空间转换为单词节点,然后将其与节点 function 切碎:

select word, COUNT(*)
from (
    select n.value('.', 'nvarchar(50)') AS word
    from (
        VALUES
        (1, 'SQL Server is a popular database management system'),
        (2, 'It is widely used for data storage and retrieval'),
        (3, 'SQL Server is a powerful tool for data analysis')
        ) AS t (id, txt)
     CROSS APPLY (
        SELECT  CAST('<x>' + REPLACE(txt, ' ', '</x><x>') + '</x>' AS XML) x
        ) x
    CROSS APPLY x.nodes('x') z(n)
    ) w
GROUP BY word

Of course, this will fail on "bad" words and invalid xml-characters but it can be worked on.当然,这将在“坏”字和无效的 xml 字符上失败,但可以解决。 Text processing has never been SQL Server's strong-point though, so probably better to use some NLP library to do this kind of stuff文本处理从来都不是 SQL 服务器的强项,所以最好使用一些 NLP 库来做这种事情

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