Extract words from a column and count frequency - sql

Question

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? I only have read-only access to my database so I can't create a self-defined function to do this.
Here's a reproducible example:
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:
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:
Word Counts
server 2
sql 2
data 1
database 1
popular 1
powerful 1
Can anyone help on this? Would be really appreciated!

Answer 1

You can make use of String_split here, such as
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;

Answer 2

You should should the string_split function -- like this
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:
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

Answer 3

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:
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. 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