Hive UDF's (User Define Function) : Text Mining

Hive UDF's for Text Mining

This projects provides to main functions

• distance - which calculates the distance between to strings based on selected algorithm (e.g Levenstein, Jaro Winkler, NGramDistance, etc.).
• suggestion - based on a text based dictionary.
• clean - clean text from whitspaces and other characters.
• urlextractor - extract first url match from text
• classifier - classify text based on a trainings set (naive bayes classifier)

Hive configuration

First you must build the JAR.

mvn package

Start the Hive CLI and add the hive-udf-textmining-1.0-SNAPSHOT.jar to the Hive class path.

hive

ADD JAR /home/dwh/projects/hive-udf/target/hive-udf-textmining-1.0-SNAPSHOT-jar-with-dependencies.jar;
CREATE TEMPORARY FUNCTION distance as 'ch.yax.hive.udf.text.Distance';
CREATE TEMPORARY FUNCTION suggestion as 'ch.yax.hive.udf.text.Suggestion';
CREATE TEMPORARY FUNCTION clean as 'ch.yax.hive.udf.text.Clean';
CREATE TEMPORARY FUNCTION urlextractor as 'ch.yax.hive.udf.text.UrlExtractor';
CREATE TEMPORARY FUNCTION classifier as 'ch.yax.hive.udf.text.TextClassifier';

CREATE TEMPORARY FUNCTION timestamp as 'ch.yax.hive.udf.number.Timestamp';
CREATE TEMPORARY FUNCTION increment as 'ch.yax.hive.udf.number.AutoIncrement';

create a table dummy and a file dual.txt with value ‘X’. The load the file into the table.

CREATE TABLE DUAL (text STRING);

LOAD DATA LOCAL INPATH '/data/dual.txt' OVERWRITE INTO TABLE DUAL;

You can now execute the query to calculate the Levenshtein distance between two strings.

SELECT distance("L", "my text", "me text") FROM DUAL;

Or for the Jaro–Winkler distance

SELECT distance("J", "my text", "me text") FROM DUAL;

Or the suggestions function which returns the best match for "football" in the file "/tmp/sports.txt" based on the Levenshtein distance.

ADD FILE /data/sport.txt;

SELECT suggestion("L", "i love football", "/data/sport.txt") FROM DUAL;

This query should return FOOTBALL. You can also add the threshold a value from 0.0 to 1.0 and the minimum token length.

SELECT suggestion("L", "i love foot", "/data/sport.txt", 0.5, 4) FROM DUAL;

float : distance (string strategy, string target, string other)

parameters:

• strategy: the algorithm which should be used for calculating the distance. L = LEVENSTEIN, J = JAROWINKLER or N2 = BIGRAM
• target: string to compare
• other: string to compare

returns: the distance between the target and other as float.

string : suggestion (string strategy, string target, string file)

parameters:

• strategy: the algorithm which should be used for calculating the distance. L = LEVENSTEIN, J = JAROWINKLER or N2 = BIGRAM
• target: string to compare
• file: a file with suggestions which should be returned when they matched.

returns: the string from the file in upper-case which has the best match with the target string or 'UNKNOW' when not match was found. As default minimum token length is 4 and match must be equal or better than a threshold 0.85.

string : suggestion (string strategy, string target, string file, float threshold, integer minTokenLength)

parameters:

• strategy: the algorithm which should be used for calculating the distance. L = LEVENSTEIN, J = JAROWINKLER or N2 = BIGRAM
• target: string to compare
• file: a file with suggestions which should be returned when they matched.
• threshold: the minimum threshold for a match
• minTokenLength: minimum token length

returns: the string from the file in upper-case which has the best match with the target string or 'UNKNOW' when not match was found.

string : clean (string text)

parameters:

• text: original text

returns: cleaned text

string : urlextractor (string text)

parameters:

• text: original text with url

returns: returns first url match

string : classifier (string text, string file)

parameters:

• text: text to classify
• file: trainings data for classification

returns: returns classified group from file

Text Mining

select clean(text), suggestion("L", clean(text),"/home/dwh/ch.place.txt") from tweets;


ADD FILE /home/dwh/trainings_data.csv;
ADD FILE /home/dwh/ch.place.txt;
select classifier(clean(text),'/home/dwh/trainings_data.csv'), clean(text) from tweets;
select classifier(clean(text),'/home/dwh/trainings_data.csv', 0.5), clean(text) from tweets;

select classifier(clean(text),'/home/dwh/trainings_data.csv', 0.5), suggestion('L', clean(text), '/home/dwh/ch.place.txt'), clean(text) from tweets;

select increment(), timestamp(), classifier(clean(text),'/home/dwh/trainings_data.csv', 0.5), suggestion('L', clean(text), '/home/dwh/ch.place.txt'), clean(text) from tweets;

insert overwrite local directory '/tmp/out' select clean(text) from tweets;

Initialize Eclipse

Initialize Eclipse

To initialize eclipse settings run the following maven command.

mvn eclipse:eclipse

Downloads:

Sample Java Code (Text Mining)

I hope this tutorial will surely help you. If you have any questions or problems please let me know.

Happy Hadooping with Patrick..