[SPARK-9057][STREAMING] Twitter example joining to static RDD of word sentiment values

Example of joining a static RDD of word sentiments to a streaming RDD of Tweets in order to demo the usage of the transform() method.

Author: Jeff L <sha0lin@alumni.carnegiemellon.edu>

Closes #8431 from Agent007/SPARK-9057.

4 files changed, 2830 insertions, 0 deletions

diff --git a/data/streaming/AFINN-111.txt b/data/streaming/AFINN-111.txt
new file mode 100644
index 0000000000..0f6fb8ebaa
--- /dev/null
+++ b/data/streaming/AFINN-111.txt
@@ -0,0 +1,2477 @@
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+slut	-5
+smart	1
+smarter	2
+smartest	2
+smear	-2
+smile	2
+smiled	2
+smiles	2
+smiling	2
+smog	-2
+sneaky	-1
+snub	-2
+snubbed	-2
+snubbing	-2
+snubs	-2
+sobering	1
+solemn	-1
+solid	2
+solidarity	2
+solution	1
+solutions	1
+solve	1
+solved	1
+solves	1
+solving	1
+somber	-2
+some kind	0
+son-of-a-bitch	-5
+soothe	3
+soothed	3
+soothing	3
+sophisticated	2
+sore	-1
+sorrow	-2
+sorrowful	-2
+sorry	-1
+spam	-2
+spammer	-3
+spammers	-3
+spamming	-2
+spark	1
+sparkle	3
+sparkles	3
+sparkling	3
+speculative	-2
+spirit	1
+spirited	2
+spiritless	-2
+spiteful	-2
+splendid	3
+sprightly	2
+squelched	-1
+stab	-2
+stabbed	-2
+stable	2
+stabs	-2
+stall	-2
+stalled	-2
+stalling	-2
+stamina	2
+stampede	-2
+startled	-2
+starve	-2
+starved	-2
+starves	-2
+starving	-2
+steadfast	2
+steal	-2
+steals	-2
+stereotype	-2
+stereotyped	-2
+stifled	-1
+stimulate	1
+stimulated	1
+stimulates	1
+stimulating	2
+stingy	-2
+stolen	-2
+stop	-1
+stopped	-1
+stopping	-1
+stops	-1
+stout	2
+straight	1
+strange	-1
+strangely	-1
+strangled	-2
+strength	2
+strengthen	2
+strengthened	2
+strengthening	2
+strengthens	2
+stressed	-2
+stressor	-2
+stressors	-2
+stricken	-2
+strike	-1
+strikers	-2
+strikes	-1
+strong	2
+stronger	2
+strongest	2
+struck	-1
+struggle	-2
+struggled	-2
+struggles	-2
+struggling	-2
+stubborn	-2
+stuck	-2
+stunned	-2
+stunning	4
+stupid	-2
+stupidly	-2
+suave	2
+substantial	1
+substantially	1
+subversive	-2
+success	2
+successful	3
+suck	-3
+sucks	-3
+suffer	-2
+suffering	-2
+suffers	-2
+suicidal	-2
+suicide	-2
+suing	-2
+sulking	-2
+sulky	-2
+sullen	-2
+sunshine	2
+super	3
+superb	5
+superior	2
+support	2
+supported	2
+supporter	1
+supporters	1
+supporting	1
+supportive	2
+supports	2
+survived	2
+surviving	2
+survivor	2
+suspect	-1
+suspected	-1
+suspecting	-1
+suspects	-1
+suspend	-1
+suspended	-1
+suspicious	-2
+swear	-2
+swearing	-2
+swears	-2
+sweet	2
+swift	2
+swiftly	2
+swindle	-3
+swindles	-3
+swindling	-3
+sympathetic	2
+sympathy	2
+tard	-2
+tears	-2
+tender	2
+tense	-2
+tension	-1
+terrible	-3
+terribly	-3
+terrific	4
+terrified	-3
+terror	-3
+terrorize	-3
+terrorized	-3
+terrorizes	-3
+thank	2
+thankful	2
+thanks	2
+thorny	-2
+thoughtful	2
+thoughtless	-2
+threat	-2
+threaten	-2
+threatened	-2
+threatening	-2
+threatens	-2
+threats	-2
+thrilled	5
+thwart	-2
+thwarted	-2
+thwarting	-2
+thwarts	-2
+timid	-2
+timorous	-2
+tired	-2
+tits	-2
+tolerant	2
+toothless	-2
+top	2
+tops	2
+torn	-2
+torture	-4
+tortured	-4
+tortures	-4
+torturing	-4
+totalitarian	-2
+totalitarianism	-2
+tout	-2
+touted	-2
+touting	-2
+touts	-2
+tragedy	-2
+tragic	-2
+tranquil	2
+trap	-1
+trapped	-2
+trauma	-3
+traumatic	-3
+travesty	-2
+treason	-3
+treasonous	-3
+treasure	2
+treasures	2
+trembling	-2
+tremulous	-2
+tricked	-2
+trickery	-2
+triumph	4
+triumphant	4
+trouble	-2
+troubled	-2
+troubles	-2
+true	2
+trust	1
+trusted	2
+tumor	-2
+twat	-5
+ugly	-3
+unacceptable	-2
+unappreciated	-2
+unapproved	-2
+unaware	-2
+unbelievable	-1
+unbelieving	-1
+unbiased	2
+uncertain	-1
+unclear	-1
+uncomfortable	-2
+unconcerned	-2
+unconfirmed	-1
+unconvinced	-1
+uncredited	-1
+undecided	-1
+underestimate	-1
+underestimated	-1
+underestimates	-1
+underestimating	-1
+undermine	-2
+undermined	-2
+undermines	-2
+undermining	-2
+undeserving	-2
+undesirable	-2
+uneasy	-2
+unemployment	-2
+unequal	-1
+unequaled	2
+unethical	-2
+unfair	-2
+unfocused	-2
+unfulfilled	-2
+unhappy	-2
+unhealthy	-2
+unified	1
+unimpressed	-2
+unintelligent	-2
+united	1
+unjust	-2
+unlovable	-2
+unloved	-2
+unmatched	1
+unmotivated	-2
+unprofessional	-2
+unresearched	-2
+unsatisfied	-2
+unsecured	-2
+unsettled	-1
+unsophisticated	-2
+unstable	-2
+unstoppable	2
+unsupported	-2
+unsure	-1
+untarnished	2
+unwanted	-2
+unworthy	-2
+upset	-2
+upsets	-2
+upsetting	-2
+uptight	-2
+urgent	-1
+useful	2
+usefulness	2
+useless	-2
+uselessness	-2
+vague	-2
+validate	1
+validated	1
+validates	1
+validating	1
+verdict	-1
+verdicts	-1
+vested	1
+vexation	-2
+vexing	-2
+vibrant	3
+vicious	-2
+victim	-3
+victimize	-3
+victimized	-3
+victimizes	-3
+victimizing	-3
+victims	-3
+vigilant	3
+vile	-3
+vindicate	2
+vindicated	2
+vindicates	2
+vindicating	2
+violate	-2
+violated	-2
+violates	-2
+violating	-2
+violence	-3
+violent	-3
+virtuous	2
+virulent	-2
+vision	1
+visionary	3
+visioning	1
+visions	1
+vitality	3
+vitamin	1
+vitriolic	-3
+vivacious	3
+vociferous	-1
+vulnerability	-2
+vulnerable	-2
+walkout	-2
+walkouts	-2
+wanker	-3
+want	1
+war	-2
+warfare	-2
+warm	1
+warmth	2
+warn	-2
+warned	-2
+warning	-3
+warnings	-3
+warns	-2
+waste	-1
+wasted	-2
+wasting	-2
+wavering	-1
+weak	-2
+weakness	-2
+wealth	3
+wealthy	2
+weary	-2
+weep	-2
+weeping	-2
+weird	-2
+welcome	2
+welcomed	2
+welcomes	2
+whimsical	1
+whitewash	-3
+whore	-4
+wicked	-2
+widowed	-1
+willingness	2
+win	4
+winner	4
+winning	4
+wins	4
+winwin	3
+wish	1
+wishes	1
+wishing	1
+withdrawal	-3
+woebegone	-2
+woeful	-3
+won	3
+wonderful	4
+woo	3
+woohoo	3
+wooo	4
+woow	4
+worn	-1
+worried	-3
+worry	-3
+worrying	-3
+worse	-3
+worsen	-3
+worsened	-3
+worsening	-3
+worsens	-3
+worshiped	3
+worst	-3
+worth	2
+worthless	-2
+worthy	2
+wow	4
+wowow	4
+wowww	4
+wrathful	-3
+wreck	-2
+wrong	-2
+wronged	-2
+wtf	-4
+yeah	1
+yearning	1
+yeees	2
+yes	1
+youthful	2
+yucky	-2
+yummy	3
+zealot	-2
+zealots	-2
+zealous	2
\ No newline at end of file
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
new file mode 100644
index 0000000000..030ee30b93
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
@@ -0,0 +1,180 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.streaming;
+
+import org.apache.commons.io.IOUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.function.FlatMapFunction;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.streaming.Duration;
+import org.apache.spark.streaming.api.java.JavaDStream;
+import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
+import org.apache.spark.streaming.api.java.JavaStreamingContext;
+import org.apache.spark.streaming.twitter.TwitterUtils;
+import scala.Tuple2;
+import twitter4j.Status;
+
+import java.io.IOException;
+import java.net.URI;
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * Displays the most positive hash tags by joining the streaming Twitter data with a static RDD of
+ * the AFINN word list (http://neuro.imm.dtu.dk/wiki/AFINN)
+ */
+public class JavaTwitterHashTagJoinSentiments {
+
+  public static void main(String[] args) throws IOException {
+    if (args.length < 4) {
+      System.err.println("Usage: JavaTwitterHashTagJoinSentiments <consumer key> <consumer secret>" +
+        " <access token> <access token secret> [<filters>]");
+      System.exit(1);
+    }
+
+    StreamingExamples.setStreamingLogLevels();
+
+    String consumerKey = args[0];
+    String consumerSecret = args[1];
+    String accessToken = args[2];
+    String accessTokenSecret = args[3];
+    String[] filters = Arrays.copyOfRange(args, 4, args.length);
+
+    // Set the system properties so that Twitter4j library used by Twitter stream
+    // can use them to generate OAuth credentials
+    System.setProperty("twitter4j.oauth.consumerKey", consumerKey);
+    System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret);
+    System.setProperty("twitter4j.oauth.accessToken", accessToken);
+    System.setProperty("twitter4j.oauth.accessTokenSecret", accessTokenSecret);
+
+    SparkConf sparkConf = new SparkConf().setAppName("JavaTwitterHashTagJoinSentiments");
+    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
+    JavaReceiverInputDStream<Status> stream = TwitterUtils.createStream(jssc, filters);
+
+    JavaDStream<String> words = stream.flatMap(new FlatMapFunction<Status, String>() {
+      @Override
+      public Iterable<String> call(Status s) {
+        return Arrays.asList(s.getText().split(" "));
+      }
+    });
+
+    JavaDStream<String> hashTags = words.filter(new Function<String, Boolean>() {
+      @Override
+      public Boolean call(String word) throws Exception {
+        return word.startsWith("#");
+      }
+    });
+
+    // Read in the word-sentiment list and create a static RDD from it
+    String wordSentimentFilePath = "data/streaming/AFINN-111.txt";
+    final JavaPairRDD<String, Double> wordSentiments = jssc.sparkContext().textFile(wordSentimentFilePath)
+      .mapToPair(new PairFunction<String, String, Double>(){
+        @Override
+        public Tuple2<String, Double> call(String line) {
+          String[] columns = line.split("\t");
+          return new Tuple2<String, Double>(columns[0],
+            Double.parseDouble(columns[1]));
+        }
+      });
+
+    JavaPairDStream<String, Integer> hashTagCount = hashTags.mapToPair(
+      new PairFunction<String, String, Integer>() {
+        @Override
+        public Tuple2<String, Integer> call(String s) {
+          // leave out the # character
+          return new Tuple2<String, Integer>(s.substring(1), 1);
+        }
+      });
+
+    JavaPairDStream<String, Integer> hashTagTotals = hashTagCount.reduceByKeyAndWindow(
+      new Function2<Integer, Integer, Integer>() {
+        @Override
+        public Integer call(Integer a, Integer b) {
+          return a + b;
+        }
+      }, new Duration(10000));
+
+    // Determine the hash tags with the highest sentiment values by joining the streaming RDD
+    // with the static RDD inside the transform() method and then multiplying
+    // the frequency of the hash tag by its sentiment value
+    JavaPairDStream<String, Tuple2<Double, Integer>> joinedTuples =
+      hashTagTotals.transformToPair(new Function<JavaPairRDD<String, Integer>,
+        JavaPairRDD<String, Tuple2<Double, Integer>>>() {
+        @Override
+        public JavaPairRDD<String, Tuple2<Double, Integer>> call(JavaPairRDD<String,
+          Integer> topicCount)
+          throws Exception {
+          return wordSentiments.join(topicCount);
+        }
+      });
+
+    JavaPairDStream<String, Double> topicHappiness = joinedTuples.mapToPair(
+      new PairFunction<Tuple2<String, Tuple2<Double, Integer>>, String, Double>() {
+        @Override
+        public Tuple2<String, Double> call(Tuple2<String,
+          Tuple2<Double, Integer>> topicAndTuplePair) throws Exception {
+          Tuple2<Double, Integer> happinessAndCount = topicAndTuplePair._2();
+          return new Tuple2<String, Double>(topicAndTuplePair._1(),
+            happinessAndCount._1() * happinessAndCount._2());
+        }
+      });
+
+    JavaPairDStream<Double, String> happinessTopicPairs = topicHappiness.mapToPair(
+      new PairFunction<Tuple2<String, Double>, Double, String>() {
+        @Override
+        public Tuple2<Double, String> call(Tuple2<String, Double> topicHappiness)
+          throws Exception {
+          return new Tuple2<Double, String>(topicHappiness._2(),
+            topicHappiness._1());
+        }
+      });
+
+    JavaPairDStream<Double, String> happiest10 = happinessTopicPairs.transformToPair(
+      new Function<JavaPairRDD<Double, String>, JavaPairRDD<Double, String>>() {
+        @Override
+        public JavaPairRDD<Double, String> call(JavaPairRDD<Double,
+          String> happinessAndTopics) throws Exception {
+          return happinessAndTopics.sortByKey(false);
+        }
+      }
+    );
+
+    // Print hash tags with the most positive sentiment values
+    happiest10.foreachRDD(new Function<JavaPairRDD<Double, String>, Void>() {
+      @Override
+      public Void call(JavaPairRDD<Double, String> happinessTopicPairs) throws Exception {
+        List<Tuple2<Double, String>> topList = happinessTopicPairs.take(10);
+        System.out.println(
+          String.format("\nHappiest topics in last 10 seconds (%s total):",
+            happinessTopicPairs.count()));
+        for (Tuple2<Double, String> pair : topList) {
+          System.out.println(
+            String.format("%s (%s happiness)", pair._2(), pair._1()));
+        }
+        return null;
+      }
+    });
+
+    jssc.start();
+    jssc.awaitTermination();
+  }
+}
diff --git a/examples/src/main/python/streaming/network_wordjoinsentiments.py b/examples/src/main/python/streaming/network_wordjoinsentiments.py
new file mode 100644
index 0000000000..b85517dfdd
--- /dev/null
+++ b/examples/src/main/python/streaming/network_wordjoinsentiments.py
@@ -0,0 +1,77 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+ Shows the most positive words in UTF8 encoded, '\n' delimited text directly received the network
+ every 5 seconds. The streaming data is joined with a static RDD of the AFINN word list
+ (http://neuro.imm.dtu.dk/wiki/AFINN)
+
+ Usage: network_wordjoinsentiments.py <hostname> <port>
+   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
+
+ To run this on your local machine, you need to first run a Netcat server
+    `$ nc -lk 9999`
+ and then run the example
+    `$ bin/spark-submit examples/src/main/python/streaming/network_wordjoinsentiments.py \
+    localhost 9999`
+"""
+
+from __future__ import print_function
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+
+
+def print_happiest_words(rdd):
+    top_list = rdd.take(5)
+    print("Happiest topics in the last 5 seconds (%d total):" % rdd.count())
+    for tuple in top_list:
+        print("%s (%d happiness)" % (tuple[1], tuple[0]))
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: network_wordjoinsentiments.py <hostname> <port>", file=sys.stderr)
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingNetworkWordJoinSentiments")
+    ssc = StreamingContext(sc, 5)
+
+    # Read in the word-sentiment list and create a static RDD from it
+    word_sentiments_file_path = "data/streaming/AFINN-111.txt"
+    word_sentiments = ssc.sparkContext.textFile(word_sentiments_file_path) \
+        .map(lambda line: tuple(line.split("\t")))
+
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
+
+    word_counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a + b)
+
+    # Determine the words with the highest sentiment values by joining the streaming RDD
+    # with the static RDD inside the transform() method and then multiplying
+    # the frequency of the words by its sentiment value
+    happiest_words = word_counts.transform(lambda rdd: word_sentiments.join(rdd)) \
+        .map(lambda (word, tuple): (word, float(tuple[0]) * tuple[1])) \
+        .map(lambda (word, happiness): (happiness, word)) \
+        .transform(lambda rdd: rdd.sortByKey(False))
+
+    happiest_words.foreachRDD(print_happiest_words)
+
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
new file mode 100644
index 0000000000..0328fa81ea
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
@@ -0,0 +1,96 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.streaming
+
+import org.apache.spark.SparkConf
+import org.apache.spark.streaming.twitter.TwitterUtils
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+
+/**
+ * Displays the most positive hash tags by joining the streaming Twitter data with a static RDD of
+ * the AFINN word list (http://neuro.imm.dtu.dk/wiki/AFINN)
+ */
+object TwitterHashTagJoinSentiments {
+  def main(args: Array[String]) {
+    if (args.length < 4) {
+      System.err.println("Usage: TwitterHashTagJoinSentiments <consumer key> <consumer secret> " +
+        "<access token> <access token secret> [<filters>]")
+      System.exit(1)
+    }
+
+    StreamingExamples.setStreamingLogLevels()
+
+    val Array(consumerKey, consumerSecret, accessToken, accessTokenSecret) = args.take(4)
+    val filters = args.takeRight(args.length - 4)
+
+    // Set the system properties so that Twitter4j library used by Twitter stream
+    // can use them to generate OAuth credentials
+    System.setProperty("twitter4j.oauth.consumerKey", consumerKey)
+    System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret)
+    System.setProperty("twitter4j.oauth.accessToken", accessToken)
+    System.setProperty("twitter4j.oauth.accessTokenSecret", accessTokenSecret)
+
+    val sparkConf = new SparkConf().setAppName("TwitterHashTagJoinSentiments")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
+    val stream = TwitterUtils.createStream(ssc, None, filters)
+
+    val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
+
+    // Read in the word-sentiment list and create a static RDD from it
+    val wordSentimentFilePath = "data/streaming/AFINN-111.txt"
+    val wordSentiments = ssc.sparkContext.textFile(wordSentimentFilePath).map { line =>
+      val Array(word, happinessValue) = line.split("\t")
+      (word, happinessValue)
+    } cache()
+
+    // Determine the hash tags with the highest sentiment values by joining the streaming RDD
+    // with the static RDD inside the transform() method and then multiplying
+    // the frequency of the hash tag by its sentiment value
+    val happiest60 = hashTags.map(hashTag => (hashTag.tail, 1))
+      .reduceByKeyAndWindow(_ + _, Seconds(60))
+      .transform{topicCount => wordSentiments.join(topicCount)}
+      .map{case (topic, tuple) => (topic, tuple._1 * tuple._2)}
+      .map{case (topic, happinessValue) => (happinessValue, topic)}
+      .transform(_.sortByKey(false))
+
+    val happiest10 = hashTags.map(hashTag => (hashTag.tail, 1))
+      .reduceByKeyAndWindow(_ + _, Seconds(10))
+      .transform{topicCount => wordSentiments.join(topicCount)}
+      .map{case (topic, tuple) => (topic, tuple._1 * tuple._2)}
+      .map{case (topic, happinessValue) => (happinessValue, topic)}
+      .transform(_.sortByKey(false))
+
+    // Print hash tags with the most positive sentiment values
+    happiest60.foreachRDD(rdd => {
+      val topList = rdd.take(10)
+      println("\nHappiest topics in last 60 seconds (%s total):".format(rdd.count()))
+      topList.foreach{case (happiness, tag) => println("%s (%s happiness)".format(tag, happiness))}
+    })
+
+    happiest10.foreachRDD(rdd => {
+      val topList = rdd.take(10)
+      println("\nHappiest topics in last 10 seconds (%s total):".format(rdd.count()))
+      topList.foreach{case (happiness, tag) => println("%s (%s happiness)".format(tag, happiness))}
+    })
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}
+// scalastyle:on println