Community Articles
Find and share helpful community-sourced technical articles
Announcements
Alert: Welcome to the Unified Cloudera Community. Former HCC members be sure to read and learn how to activate your account here.
New Contributor

This article is designed to extend my articles Twitter Sentiment using Spark Core NLP in Apache Zeppelin and Connecting Solr to Spark - Apache Zeppelin Notebook

I have included the complete notebook on my Github site, which can be found on my GitHub site.

Step 1 - Follow the tutorial in the provide articles above, and establish an Apache Solr collection called "tweets"

Step 2 - Verify the version of Apache Spark being used, and visit the Solr-Spark connector site. The key is to match the version of Spark the version of the Solr-Spark connector. In the example below, the version of Spark is 2.2.0, and the connector version is 3.4.4

%spark2
sc
sc.version
</p><p>
Step 3 - Include the Solr-Spark dependency in Zeppelin. Important note: This needs to be run before
the Spark Context has been initialized</p>
<pre>%dep 
z.load("com.lucidworks.spark:spark-solr:jar:3.4.4")
//Must be used before SparkInterpreter (%spark2) initialized
//Hint: put this paragraph before any Spark code and restart Zeppelin/Interpreter

Step 4 - Download the Stanford CoreNLP libraries found on here: <a href="http://nlp.stanford.edu/software/stanford-corenlp-full-2018-02-27.zip">http://nlp.stanford.edu/software/stanford-corenlp-full-2018-02-27.zip</a>

Upzip the download and move it to the /tmp directory. Note: This can be accomplished on the command line or the following Zeppelin paragraph will work as well

%sh 
wget /tmp/stanford-corenlp-full-2018-02-27.zip http://nlp.stanford.edu/software/stanford-corenlp-full-2018-02-27.zip unzip /tmp/stanford-corenlp-full-2018-02-27.zip

Step 5 - In Zeppelin's Interpreters configurations for Spark, include the following artifact: /tmp/stanford-corenlp-full-2018-02-27/stanford-corenlp-3.9.1-models.jar

83523-screen-shot-2018-05-23-at-122025-pm.png

Step 6 - Include the following Spark dependencies for Stanford CoreNLP and Spark CoreNLP. Important note: This needs to be run before the Spark Context has been initialized

%dep
z.load("edu.stanford.nlp:stanford-corenlp:3.9.1")
//In Spark Interper Settings Add the following artifact
// /tmp/stanford-corenlp-full-2018-02-27/stanford-corenlp-3.9.1-models.jar
%dep
z.load("databricks:spark-corenlp:0.2.0-s_2.11")

Step 7 Include the following Spark dependencies for JPMML-SparkML and JPMML-Model. Important note: This needs to be run before the Spark Context has been initialized.

%dep
z.load("org.jpmml:jpmml-sparkml:1.4.5")

</p><pre>%dep
z.load("org.jpmml:pmml-model:1.4.5")

Step 8 - Run Solr query and return results into Spark DataFrame. Note: Zookeeper host might need to use full names: "zkhost" -> "host-1.domain.com:2181,host-2.domain.com:2181,host-3.domain.com:2181/solr"

%spark2 
val options = Map( "collection" -> 
 "Tweets", "zkhost" -> "localhost:2181/solr", 
 // "query" -> "Keyword, 'More Keywords'" 
) 
val df = spark.read.format("solr").options(options).load df.cache()
</p><p>
 Step 9 - Review results of the Solr query</p><pre>%spark2 
df.count() 
df.printSchema() 
df.take(1)
</p><p>
 Step 10 - Filter the
Tweets in the Spark DataFrame to ensure the Tweet text isn't null Once filter
has been completed, add the sentiment value to the tweets.</p>
<pre>%spark2 
import org.apache.spark.sql.functions._ 
import org.apache.spark.sql.types._
import com.databricks.spark.corenlp.functions._ 
val df_TweetSentiment = df.filter("text_t is not null").select($"text_t", sentiment($"text_t").as('sentimentScore))

Step 11 - Valid results

%spark2 
df_TweetSentiment.cache() 
df_TweetSentiment1.printSchema() 
df_TweetSentiment1.take(1) 
df_TweetSentiment1.count()

Step 12 - Build Stages to build features that will be fed into a Logistic Regression model for classification

Stage 1 -Regex Tokenizer will be used to separate each word into individual "tokens"

Stage 2 -Count Vectorizer will count the number of occurrences each token occurs in the text corpus

Stage 3 -Term frequency-inverse document frequency (TF-IDF) is a feature vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus.

Stage 4 -Logistic Regression for classification to predict sentiment score

%spark2
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer, RegexTokenizer, CountVectorizer, CountVectorizerModel}
import org.apache.spark.ml.classification.LogisticRegression
val tokenizer = new RegexTokenizer()
.setInputCol("text_t")
.setOutputCol("words")
.setPattern("\W+")
.setGaps(true)
val wordsData = tokenizer.transform(df_TweetSentiment)
val cvModel = new CountVectorizer()
.setInputCol("words").setOutputCol("rawFeatures")
.setMinDF(4)
.fit(wordsData)
val featurizedData = cvModel.transform(wordsData)
val idf = new IDF()
.setInputCol("rawFeatures")
.setOutputCol("features")
val idfModel = idf.fit(featurizedData)
val rescaledData = idfModel.transform(featurizedData)
rescaledData.select("sentimentScore", "features").show()
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.001)
.setLabelCol("sentimentScore")

Step 13 - Build Spark Pipeline from Stages

%spark2
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.StructType
val pipeline = new Pipeline()
.setStages(Array(tokenizer, cvModel, idfModel, lr))
val PipeLineModel = pipeline.fit(df_TweetSentiment)
//Save Pipeline to Disk (Optional)
PipeLineModel.write.overwrite().save("/tmp/spark-IDF-model")
val schema = df_TweetSentiment.schema

Step 14 - Export Spark Pipeline to PMML using JPMML-SparkML

%spark2
import org.jpmml.sparkml.PMMLBuilder
import java.io.File
val pmml = new PMMLBuilder(schema, PipeLineModel)
val file = pmml.buildFile(new File("/tmp/TweetPipeline.pmml"))
1,079 Views
Comments
New Contributor

Hi Ian! Thanks for posting this article.

At the "pmml.buildFile" I'm getting the following error: java.lang.IllegalArgumentException: iperbole_

Any ideas? Thank you very much!

Don't have an account?
Coming from Hortonworks? Activate your account here
Version history
Revision #:
2 of 2
Last update:
‎08-17-2019 06:52 AM
Updated by:
 
Contributors
Top Kudoed Authors