From 6bf99694c641bbc8afaa1ea627fbf3991dfb5501 Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Tue, 25 Aug 2015 12:47:18 -0700
Subject: [PATCH 1/7] [SPARK-9057] [STREAMING] 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.

---
 .../main/resources/twitter_sentiment_list.txt | 7471 +++++++++++++++++
 .../streaming/TwitterTagSentiments.scala      |   89 +
 2 files changed, 7560 insertions(+)
 create mode 100755 examples/src/main/resources/twitter_sentiment_list.txt
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala

diff --git a/examples/src/main/resources/twitter_sentiment_list.txt b/examples/src/main/resources/twitter_sentiment_list.txt
new file mode 100755
index 0000000000000..89a7b85c1917b
--- /dev/null
+++ b/examples/src/main/resources/twitter_sentiment_list.txt
@@ -0,0 +1,7471 @@
+:),-1.97513,-23.69606
+...,-4.50578,-5.13896
+..,-4.53833,-5.33159
+good,-4.96792,-7.04730
+love,-5.08013,-7.27937
+!!,-5.11094,-6.29471
+!!!,-5.37589,-6.42193
+thanks,-5.39538,-8.48155
+lol,-5.46376,-6.85811
+haha,-5.52428,-7.07890
+day,-5.60124,-6.88708
+happy,-5.63706,-8.43618
+now,-5.66081,-6.39297
+:d,-5.70703,-7.18731
+get,-5.70729,-6.50039
+follow,-5.75256,-8.27970
+time,-5.81191,-7.03576
+morning,-5.84729,-7.82800
+night,-5.94634,-7.35459
+today,-5.95127,-6.84772
+got,-6.01746,-6.90601
+know,-6.02247,-6.96152
+thank,-6.10824,-9.39810
+going,-6.12724,-6.98902
+great,-6.17118,-8.48979
+new,-6.17728,-7.90633
+well,-6.18825,-7.41401
+hope,-6.32218,-7.58988
+nice,-6.32451,-8.56744
+please,-6.35263,-7.64079
+hey,-6.37790,-8.52206
+birthday,-6.39928,-9.10983
+think,-6.42011,-7.27946
+home,-6.44255,-7.21934
+twitter,-6.45411,-7.72104
+best,-6.45556,-8.55717
+really,-6.45737,-6.86111
+??,-6.46796,-7.19116
+much,-6.47572,-7.28604
+hehe,-6.53812,-8.60609
+fun,-6.54533,-8.18888
+tonight,-6.56378,-7.74148
+hahaha,-6.58556,-7.89611
+make,-6.59633,-7.74245
+tomorrow,-6.60396,-7.45274
+yes,-6.61660,-8.37829
+come,-6.63053,-7.53297
+wait,-6.64142,-8.32365
+work,-6.65086,-6.82528
+want,-6.66179,-6.77996
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+yeah,-6.66701,-7.98460
+need,-6.68246,-7.05372
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+done,-6.69368,-8.43531
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+still,-6.74914,-7.03173
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+amazing,-6.91375,-9.30330
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diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
new file mode 100644
index 0000000000000..c4bfad2ab24a0
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
@@ -0,0 +1,89 @@
+/*
+ * 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, SparkContext}
+import org.apache.spark.streaming.twitter.TwitterUtils
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+
+/**
+ * Inspired by TwitterPopularTags, this example program displays the most positive
+ * hash tags by joining the streaming twitter data with a static RDD of
+ * the word-sentiment file provided by http://alexdavies.net/twitter-sentiment-analysis/
+ */
+object TwitterTagSentiments {
+  def main(args: Array[String]) {
+    if (args.length < 4) {
+      System.err.println("Usage: TwitterTagSentiments <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)
+
+    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("TwitterTagSentiments")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
+    val stream = TwitterUtils.createStream(ssc, None, filters)
+
+    val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
+    val wordSentimentFilePath = "examples/src/main/resources/twitter_sentiment_list.txt"
+    val wordSentiments = ssc.sparkContext.textFile(wordSentimentFilePath).map { line =>
+      val Array(word, happinessValue, _) = line.split(",")
+      (word, happinessValue)
+    }
+
+    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))
+
+    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

From c8a2dd49e9e83fac8549281908f930aace4a1a36 Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Wed, 26 Aug 2015 11:52:57 -0700
Subject: [PATCH 2/7] [SPARK-9057] [STREAMING] Java Twitter 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.

---
 .../streaming/JavaTwitterTagSentiments.java   | 172 ++++++++++++++++++
 1 file changed, 172 insertions(+)
 create mode 100644 examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java

diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
new file mode 100644
index 0000000000000..4c641de55af45
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
@@ -0,0 +1,172 @@
+/*
+ * 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.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.util.Arrays;
+import java.util.List;
+
+/**
+ * Inspired by TwitterPopularTags, this example program displays the most positive
+ * hash tags by joining the streaming twitter data with a static RDD of
+ * the word-sentiment file provided by http://alexdavies.net/twitter-sentiment-analysis/
+ */
+public class JavaTwitterTagSentiments {
+    private JavaTwitterTagSentiments() {
+    }
+
+    public static void main(String[] args) {
+        if (args.length < 4) {
+            System.err.println("Usage: JavaTwitterTagSentiments <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);
+
+        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("JavaTwitterTagSentiments");
+        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("#");
+            }
+        });
+
+        String wordSentimentFilePath = "examples/src/main/resources/twitter_sentiment_list.txt";
+        final JavaPairRDD<String, Double> wordSentiments = jssc.sc().textFile(wordSentimentFilePath)
+                .mapToPair(new PairFunction<String, String, Double>(){
+                   @Override
+                    public Tuple2<String, Double> call(String line) {
+                       String[] columns = line.split(",");
+                       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));
+
+        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> happiest60 = 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();
+                    }
+                }
+        );
+
+        happiest60.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();
+    }
+}

From 196bedd1a613ad30b8e40bd060eb6361b94dadcc Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Mon, 31 Aug 2015 14:32:34 -0700
Subject: [PATCH 3/7] [SPARK-9057] [STREAMING] Python example of joining a
 static RDD of word sentiments to a streaming RDD of words in order to demo
 the usage of the transform() method. Because there currently isn't a Python
 equivalent of TwitterUtils, I opted to use the streaming network_wordcount.py
 example for inspiration.

---
 .../streaming/network_wordsentiments.py       | 72 +++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 examples/src/main/python/streaming/network_wordsentiments.py

diff --git a/examples/src/main/python/streaming/network_wordsentiments.py b/examples/src/main/python/streaming/network_wordsentiments.py
new file mode 100644
index 0000000000000..8306c9e8375c2
--- /dev/null
+++ b/examples/src/main/python/streaming/network_wordsentiments.py
@@ -0,0 +1,72 @@
+#
+# 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 word-sentiment file
+ provided by http://alexdavies.net/twitter-sentiment-analysis/
+
+ Usage: network_wordcount.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_wordsentiments.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_wordsentiments.py <hostname> <port>", file=sys.stderr)
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingNetworkWordSentiments")
+    ssc = StreamingContext(sc, 5)
+
+    file_path = "examples/src/main/resources/twitter_sentiment_list.txt"
+    word_sentiments = ssc.sparkContext.textFile(file_path) \
+        .map(lambda line: tuple(line.split(",")[:-1]))
+
+    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)
+
+    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())
+
+    happiest_words.foreachRDD(print_happiest_words)
+
+    ssc.start()
+    ssc.awaitTermination()

From 7277230c9ff5c192e760bd1da7655b10b8c520aa Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Mon, 31 Aug 2015 23:21:35 -0700
Subject: [PATCH 4/7] [SPARK-9057] [STREAMING] As brennanyork suggested, the
 word-sentiment list file is downloaded during execution of the example
 streaming programs instead of relying on a file committed to the repository.
 Other miscellaneous changes include: using the AFINN word list, correcting
 the sort order of displaying the most positive words, correcting the variable
 name from happiest60 to happiest10, and code formatting

---
 .../streaming/JavaTwitterTagSentiments.java   |   34 +-
 .../streaming/network_wordsentiments.py       |   15 +-
 .../main/resources/twitter_sentiment_list.txt | 7471 -----------------
 .../streaming/TwitterTagSentiments.scala      |   16 +-
 4 files changed, 38 insertions(+), 7498 deletions(-)
 delete mode 100755 examples/src/main/resources/twitter_sentiment_list.txt

diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
index 4c641de55af45..d384cab52ce57 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
@@ -17,6 +17,7 @@
 
 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;
@@ -32,19 +33,20 @@
 import scala.Tuple2;
 import twitter4j.Status;
 
+import java.io.IOException;
+import java.net.URI;
 import java.util.Arrays;
 import java.util.List;
 
 /**
- * Inspired by TwitterPopularTags, this example program displays the most positive
- * hash tags by joining the streaming twitter data with a static RDD of
- * the word-sentiment file provided by http://alexdavies.net/twitter-sentiment-analysis/
+ * 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 JavaTwitterTagSentiments {
     private JavaTwitterTagSentiments() {
     }
 
-    public static void main(String[] args) {
+    public static void main(String[] args) throws IOException {
         if (args.length < 4) {
             System.err.println("Usage: JavaTwitterTagSentiments <consumer key> <consumer secret>" +
                     " <access token> <access token secret> [<filters>]");
@@ -82,13 +84,17 @@ public Boolean call(String word) throws Exception {
             }
         });
 
-        String wordSentimentFilePath = "examples/src/main/resources/twitter_sentiment_list.txt";
-        final JavaPairRDD<String, Double> wordSentiments = jssc.sc().textFile(wordSentimentFilePath)
+        String wordSentimentURI =
+                "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt";
+        String[] wordSentimentLines = IOUtils.toString(URI.create(wordSentimentURI)).split("\n");
+        final JavaPairRDD<String, Double> wordSentiments = jssc.sparkContext().parallelize(
+                Arrays.asList(wordSentimentLines))
                 .mapToPair(new PairFunction<String, String, Double>(){
                    @Override
                     public Tuple2<String, Double> call(String line) {
-                       String[] columns = line.split(",");
-                       return new Tuple2<String, Double>(columns[0], Double.parseDouble(columns[1]));
+                       String[] columns = line.split("\t");
+                       return new Tuple2<String, Double>(columns[0],
+                               Double.parseDouble(columns[1]));
                    }
                 });
 
@@ -109,9 +115,9 @@ public Integer call(Integer a, Integer b) {
                     }
                 }, new Duration(10000));
 
-        JavaPairDStream<String, Tuple2<Double, Integer>> joinedTuples = hashTagTotals.transformToPair(
-                new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String,
-                        Tuple2<Double, Integer>>>() {
+        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)
@@ -141,17 +147,17 @@ public Tuple2<Double, String> call(Tuple2<String, Double> topicHappiness)
             }
         });
 
-        JavaPairDStream<Double, String> happiest60 = happinessTopicPairs.transformToPair(
+        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();
+                        return happinessAndTopics.sortByKey(false);
                     }
                 }
         );
 
-        happiest60.foreachRDD(new Function<JavaPairRDD<Double, String>, Void>() {
+        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);
diff --git a/examples/src/main/python/streaming/network_wordsentiments.py b/examples/src/main/python/streaming/network_wordsentiments.py
index 8306c9e8375c2..4dd579056e178 100644
--- a/examples/src/main/python/streaming/network_wordsentiments.py
+++ b/examples/src/main/python/streaming/network_wordsentiments.py
@@ -17,8 +17,8 @@
 
 """
  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 word-sentiment file
- provided by http://alexdavies.net/twitter-sentiment-analysis/
+ 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_wordcount.py <hostname> <port>
    <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
@@ -32,6 +32,7 @@
 from __future__ import print_function
 
 import sys
+import urllib2
 
 from pyspark import SparkContext
 from pyspark.streaming import StreamingContext
@@ -51,9 +52,11 @@ def print_happiest_words(rdd):
     sc = SparkContext(appName="PythonStreamingNetworkWordSentiments")
     ssc = StreamingContext(sc, 5)
 
-    file_path = "examples/src/main/resources/twitter_sentiment_list.txt"
-    word_sentiments = ssc.sparkContext.textFile(file_path) \
-        .map(lambda line: tuple(line.split(",")[:-1]))
+    word_sentiments_uri = "http://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/" + \
+                          "AFINN-111.txt"
+    word_sentiments_lines = urllib2.urlopen(word_sentiments_uri).read().split("\n")
+    word_sentiments = ssc.sparkContext.parallelize(word_sentiments_lines) \
+        .map(lambda line: tuple(line.split("\t")))
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
 
@@ -64,7 +67,7 @@ def print_happiest_words(rdd):
     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())
+        .transform(lambda rdd: rdd.sortByKey(False))
 
     happiest_words.foreachRDD(print_happiest_words)
 
diff --git a/examples/src/main/resources/twitter_sentiment_list.txt b/examples/src/main/resources/twitter_sentiment_list.txt
deleted file mode 100755
index 89a7b85c1917b..0000000000000
--- a/examples/src/main/resources/twitter_sentiment_list.txt
+++ /dev/null
@@ -1,7471 +0,0 @@
-:),-1.97513,-23.69606
-...,-4.50578,-5.13896
-..,-4.53833,-5.33159
-good,-4.96792,-7.04730
-love,-5.08013,-7.27937
-!!,-5.11094,-6.29471
-!!!,-5.37589,-6.42193
-thanks,-5.39538,-8.48155
-lol,-5.46376,-6.85811
-haha,-5.52428,-7.07890
-day,-5.60124,-6.88708
-happy,-5.63706,-8.43618
-now,-5.66081,-6.39297
-:d,-5.70703,-7.18731
-get,-5.70729,-6.50039
-follow,-5.75256,-8.27970
-time,-5.81191,-7.03576
-morning,-5.84729,-7.82800
-night,-5.94634,-7.35459
-today,-5.95127,-6.84772
-got,-6.01746,-6.90601
-know,-6.02247,-6.96152
-thank,-6.10824,-9.39810
-going,-6.12724,-6.98902
-great,-6.17118,-8.48979
-new,-6.17728,-7.90633
-well,-6.18825,-7.41401
-hope,-6.32218,-7.58988
-nice,-6.32451,-8.56744
-please,-6.35263,-7.64079
-hey,-6.37790,-8.52206
-birthday,-6.39928,-9.10983
-think,-6.42011,-7.27946
-home,-6.44255,-7.21934
-twitter,-6.45411,-7.72104
-best,-6.45556,-8.55717
-really,-6.45737,-6.86111
-??,-6.46796,-7.19116
-much,-6.47572,-7.28604
-hehe,-6.53812,-8.60609
-fun,-6.54533,-8.18888
-tonight,-6.56378,-7.74148
-hahaha,-6.58556,-7.89611
-make,-6.59633,-7.74245
-tomorrow,-6.60396,-7.45274
-yes,-6.61660,-8.37829
-come,-6.63053,-7.53297
-wait,-6.64142,-8.32365
-work,-6.65086,-6.82528
-want,-6.66179,-6.77996
-iya,-6.66362,-7.81330
-better,-6.66592,-7.86455
-yeah,-6.66701,-7.98460
-need,-6.68246,-7.05372
-watching,-6.69118,-8.43455
-done,-6.69368,-8.43531
-right,-6.71856,-7.54179
-still,-6.74914,-7.03173
-say,-6.74983,-8.07015
-way,-6.75280,-7.94918
-weekend,-6.76175,-7.96880
-gonna,-6.76612,-7.62698
-aja,-6.83063,-7.60181
-last,-6.83397,-7.33366
-makasih,-6.84987,-10.42759
-kak,-6.85964,-8.54986
-awesome,-6.86298,-9.27465
-always,-6.86934,-8.15587
-yaa,-6.90635,-8.52593
-selamat,-6.91255,-10.32133
-amazing,-6.91375,-9.30330
-let,-6.92211,-8.35869
-tweet,-6.93889,-8.27581
-followed,-6.94123,-10.34797
-miss,-6.94468,-6.43117
-soon,-6.96003,-8.01957
-next,-6.97510,-8.05371
-people,-6.98183,-8.09795
-guys,-6.98655,-8.46691
-sure,-6.99089,-8.70173
-sleep,-6.99996,-7.28043
-welcome,-7.00215,-10.69495
-life,-7.01730,-8.19512
-okay,-7.01881,-8.56829
-everyone,-7.02944,-8.59162
-lagi,-7.03050,-7.51641
-never,-7.03476,-7.55595
-feel,-7.04362,-6.97223
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-$10,-11.77732,-13.35487
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-zetten,-11.78330,-13.03534
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-miserable,-11.84869,-10.95559
-wack,-11.84881,-12.06347
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-lopen,-11.85115,-12.12921
-creer,-11.85211,-11.35572
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-plant,-11.85804,-12.51088
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-brrr,-11.86321,-11.86856
-playoffs,-11.86340,-12.43532
-pills,-11.86828,-11.78802
-dinero,-11.87012,-11.42624
-stood,-11.87015,-11.93781
-tourney,-11.87074,-12.91836
-aaai,-11.87205,-10.77274
-iih,-11.87244,-11.57280
-disgusting,-11.87323,-11.43823
-bier,-11.87327,-13.08593
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-earthquake,-11.89158,-11.78341
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-vinden,-11.89657,-12.52595
-revising,-11.89799,-11.21437
-menina,-11.90202,-12.13601
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-=s,-11.94710,-12.17077
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-desculpa,-11.94971,-10.88096
--),-11.95289,-13.30405
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-ziek,-12.45351,-10.69471
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-tristeza,-12.53682,-11.03935
-disappointing,-12.54262,-11.07419
-deixou,-12.55057,-11.26587
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-sozinha,-12.55475,-10.75526
-infection,-12.59157,-11.03206
-frustrated,-12.59354,-11.43860
-coughing,-12.61219,-10.84748
-migraine,-12.61578,-10.96128
-unfair,-12.64858,-11.07637
-sux,-12.68774,-11.22109
-damnit,-12.68893,-11.35463
-nfl,-12.69165,-12.85613
-ficando,-12.74027,-11.55447
-allergies,-12.74101,-11.01884
-perih,-12.77916,-10.97610
-sozinho,-12.78075,-11.80948
-pqp,-12.80167,-11.28881
-pijn,-12.83267,-10.64725
-bleeding,-12.95056,-11.25376
-cramps,-12.95525,-11.00697
-ngm,-13.01938,-10.89306
-doente,-13.03306,-10.95610
-chorar,-13.09843,-11.04243
-poorly,-13.19811,-11.07588
-droga,-13.28826,-10.91051
-tsunami,-13.34616,-13.03963
-nuclear,-13.38295,-14.20697
-mimimi,-14.35933,-10.63735
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
index c4bfad2ab24a0..b04f828f3991b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
@@ -23,9 +23,8 @@ import org.apache.spark.streaming.twitter.TwitterUtils
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 
 /**
- * Inspired by TwitterPopularTags, this example program displays the most positive
- * hash tags by joining the streaming twitter data with a static RDD of
- * the word-sentiment file provided by http://alexdavies.net/twitter-sentiment-analysis/
+ * 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 TwitterTagSentiments {
   def main(args: Array[String]) {
@@ -50,11 +49,14 @@ object TwitterTagSentiments {
     val stream = TwitterUtils.createStream(ssc, None, filters)
 
     val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
-    val wordSentimentFilePath = "examples/src/main/resources/twitter_sentiment_list.txt"
-    val wordSentiments = ssc.sparkContext.textFile(wordSentimentFilePath).map { line =>
-      val Array(word, happinessValue, _) = line.split(",")
+
+    val wordSentimentURI =
+      "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt"
+    val wordSentimentLines = scala.io.Source.fromURL(wordSentimentURI).mkString.split("\n")
+    val wordSentiments = ssc.sparkContext.parallelize(wordSentimentLines).map { line =>
+      val Array(word, happinessValue) = line.split("\t")
       (word, happinessValue)
-    }
+    } cache()
 
     val happiest60 = hashTags.map(hashTag => (hashTag.tail, 1))
       .reduceByKeyAndWindow(_ + _, Seconds(60))

From fd498d41c83d1c065853d3dafe0c1026fba46279 Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Tue, 22 Sep 2015 17:02:38 -0700
Subject: [PATCH 5/7] [SPARK-9057] [STREAMING] Removed superfluous private
 constructor per feedback from tdas

---
 .../spark/examples/streaming/JavaTwitterTagSentiments.java      | 2 --
 1 file changed, 2 deletions(-)

diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
index d384cab52ce57..e8b0e94984802 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
@@ -43,8 +43,6 @@
  * the AFINN word list (http://neuro.imm.dtu.dk/wiki/AFINN)
  */
 public class JavaTwitterTagSentiments {
-    private JavaTwitterTagSentiments() {
-    }
 
     public static void main(String[] args) throws IOException {
         if (args.length < 4) {

From fbcde0ca793f113ab39f9695f1c22a2cf7f7e62e Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Tue, 22 Sep 2015 19:12:58 -0700
Subject: [PATCH 6/7] [SPARK-9057] [STREAMING]

Based on feedback from tdas, the relevant example class names now include the word "join" in order to be clearer that the example shows how to join a streaming RDD; the Java example is reformatted to use 2 spaces for indentation; and, more comments are included.
---
 .../JavaTwitterHashTagJoinSentiments.java     | 183 ++++++++++++++++++
 .../streaming/JavaTwitterTagSentiments.java   | 176 -----------------
 ...ments.py => network_wordjoinsentiments.py} |  13 +-
 ...ala => TwitterHashTagJoinSentiments.scala} |  13 +-
 4 files changed, 202 insertions(+), 183 deletions(-)
 create mode 100644 examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
 delete mode 100644 examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
 rename examples/src/main/python/streaming/{network_wordsentiments.py => network_wordjoinsentiments.py} (82%)
 rename examples/src/main/scala/org/apache/spark/examples/streaming/{TwitterTagSentiments.scala => TwitterHashTagJoinSentiments.scala} (83%)

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 0000000000000..0449095583a04
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
@@ -0,0 +1,183 @@
+/*
+ * 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 wordSentimentURI =
+      "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt";
+    String[] wordSentimentLines = IOUtils.toString(URI.create(wordSentimentURI)).split("\n");
+    final JavaPairRDD<String, Double> wordSentiments = jssc.sparkContext().parallelize(
+      Arrays.asList(wordSentimentLines))
+      .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/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
deleted file mode 100644
index e8b0e94984802..0000000000000
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterTagSentiments.java
+++ /dev/null
@@ -1,176 +0,0 @@
-/*
- * 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 JavaTwitterTagSentiments {
-
-    public static void main(String[] args) throws IOException {
-        if (args.length < 4) {
-            System.err.println("Usage: JavaTwitterTagSentiments <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);
-
-        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("JavaTwitterTagSentiments");
-        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("#");
-            }
-        });
-
-        String wordSentimentURI =
-                "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt";
-        String[] wordSentimentLines = IOUtils.toString(URI.create(wordSentimentURI)).split("\n");
-        final JavaPairRDD<String, Double> wordSentiments = jssc.sparkContext().parallelize(
-                Arrays.asList(wordSentimentLines))
-                .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));
-
-        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);
-                    }
-                }
-        );
-
-        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_wordsentiments.py b/examples/src/main/python/streaming/network_wordjoinsentiments.py
similarity index 82%
rename from examples/src/main/python/streaming/network_wordsentiments.py
rename to examples/src/main/python/streaming/network_wordjoinsentiments.py
index 4dd579056e178..385fd4a48f53e 100644
--- a/examples/src/main/python/streaming/network_wordsentiments.py
+++ b/examples/src/main/python/streaming/network_wordjoinsentiments.py
@@ -20,13 +20,14 @@
  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_wordcount.py <hostname> <port>
+ 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_wordsentiments.py localhost 9999`
+    `$ bin/spark-submit examples/src/main/python/streaming/network_wordjoinsentiments.py \
+    localhost 9999`
 """
 
 from __future__ import print_function
@@ -46,12 +47,13 @@ def print_happiest_words(rdd):
 
 if __name__ == "__main__":
     if len(sys.argv) != 3:
-        print("Usage: network_wordsentiments.py <hostname> <port>", file=sys.stderr)
+        print("Usage: network_wordjoinsentiments.py <hostname> <port>", file=sys.stderr)
         exit(-1)
 
-    sc = SparkContext(appName="PythonStreamingNetworkWordSentiments")
+    sc = SparkContext(appName="PythonStreamingNetworkWordJoinSentiments")
     ssc = StreamingContext(sc, 5)
 
+    # Read in the word-sentiment list and create a static RDD from it
     word_sentiments_uri = "http://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/" + \
                           "AFINN-111.txt"
     word_sentiments_lines = urllib2.urlopen(word_sentiments_uri).read().split("\n")
@@ -64,6 +66,9 @@ def print_happiest_words(rdd):
         .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)) \
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
similarity index 83%
rename from examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
index b04f828f3991b..61303ec9be42d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterTagSentiments.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
@@ -26,10 +26,10 @@ 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 TwitterTagSentiments {
+object TwitterHashTagJoinSentiments {
   def main(args: Array[String]) {
     if (args.length < 4) {
-      System.err.println("Usage: TwitterTagSentiments <consumer key> <consumer secret> " +
+      System.err.println("Usage: TwitterHashTagJoinSentiments <consumer key> <consumer secret> " +
         "<access token> <access token secret> [<filters>]")
       System.exit(1)
     }
@@ -39,17 +39,20 @@ object TwitterTagSentiments {
     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("TwitterTagSentiments")
+    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 wordSentimentURI =
       "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt"
     val wordSentimentLines = scala.io.Source.fromURL(wordSentimentURI).mkString.split("\n")
@@ -58,6 +61,9 @@ object TwitterTagSentiments {
       (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)}
@@ -72,6 +78,7 @@ object TwitterTagSentiments {
       .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()))

From 9998df67b1c15c7cc350ef59c10af8275b947e02 Mon Sep 17 00:00:00 2001
From: Jeff L <sha0lin@alumni.carnegiemellon.edu>
Date: Wed, 23 Sep 2015 08:51:46 -0700
Subject: [PATCH 7/7] [SPARK-9057] [STREAMING]

The word sentiment file for the streaming join examples is included in data/streaming as suggested by tdas
---
 data/streaming/AFINN-111.txt                  | 2477 +++++++++++++++++
 .../JavaTwitterHashTagJoinSentiments.java     |    7 +-
 .../streaming/network_wordjoinsentiments.py   |    7 +-
 .../TwitterHashTagJoinSentiments.scala        |    8 +-
 4 files changed, 2484 insertions(+), 15 deletions(-)
 create mode 100644 data/streaming/AFINN-111.txt

diff --git a/data/streaming/AFINN-111.txt b/data/streaming/AFINN-111.txt
new file mode 100644
index 0000000000000..0f6fb8ebaa0bf
--- /dev/null
+++ b/data/streaming/AFINN-111.txt
@@ -0,0 +1,2477 @@
+abandon	-2
+abandoned	-2
+abandons	-2
+abducted	-2
+abduction	-2
+abductions	-2
+abhor	-3
+abhorred	-3
+abhorrent	-3
+abhors	-3
+abilities	2
+ability	2
+aboard	1
+absentee	-1
+absentees	-1
+absolve	2
+absolved	2
+absolves	2
+absolving	2
+absorbed	1
+abuse	-3
+abused	-3
+abuses	-3
+abusive	-3
+accept	1
+accepted	1
+accepting	1
+accepts	1
+accident	-2
+accidental	-2
+accidentally	-2
+accidents	-2
+accomplish	2
+accomplished	2
+accomplishes	2
+accusation	-2
+accusations	-2
+accuse	-2
+accused	-2
+accuses	-2
+accusing	-2
+ache	-2
+achievable	1
+aching	-2
+acquit	2
+acquits	2
+acquitted	2
+acquitting	2
+acrimonious	-3
+active	1
+adequate	1
+admire	3
+admired	3
+admires	3
+admiring	3
+admit	-1
+admits	-1
+admitted	-1
+admonish	-2
+admonished	-2
+adopt	1
+adopts	1
+adorable	3
+adore	3
+adored	3
+adores	3
+advanced	1
+advantage	2
+advantages	2
+adventure	2
+adventures	2
+adventurous	2
+affected	-1
+affection	3
+affectionate	3
+afflicted	-1
+affronted	-1
+afraid	-2
+aggravate	-2
+aggravated	-2
+aggravates	-2
+aggravating	-2
+aggression	-2
+aggressions	-2
+aggressive	-2
+aghast	-2
+agog	2
+agonise	-3
+agonised	-3
+agonises	-3
+agonising	-3
+agonize	-3
+agonized	-3
+agonizes	-3
+agonizing	-3
+agree	1
+agreeable	2
+agreed	1
+agreement	1
+agrees	1
+alarm	-2
+alarmed	-2
+alarmist	-2
+alarmists	-2
+alas	-1
+alert	-1
+alienation	-2
+alive	1
+allergic	-2
+allow	1
+alone	-2
+amaze	2
+amazed	2
+amazes	2
+amazing	4
+ambitious	2
+ambivalent	-1
+amuse	3
+amused	3
+amusement	3
+amusements	3
+anger	-3
+angers	-3
+angry	-3
+anguish	-3
+anguished	-3
+animosity	-2
+annoy	-2
+annoyance	-2
+annoyed	-2
+annoying	-2
+annoys	-2
+antagonistic	-2
+anti	-1
+anticipation	1
+anxiety	-2
+anxious	-2
+apathetic	-3
+apathy	-3
+apeshit	-3
+apocalyptic	-2
+apologise	-1
+apologised	-1
+apologises	-1
+apologising	-1
+apologize	-1
+apologized	-1
+apologizes	-1
+apologizing	-1
+apology	-1
+appalled	-2
+appalling	-2
+appease	2
+appeased	2
+appeases	2
+appeasing	2
+applaud	2
+applauded	2
+applauding	2
+applauds	2
+applause	2
+appreciate	2
+appreciated	2
+appreciates	2
+appreciating	2
+appreciation	2
+apprehensive	-2
+approval	2
+approved	2
+approves	2
+ardent	1
+arrest	-2
+arrested	-3
+arrests	-2
+arrogant	-2
+ashame	-2
+ashamed	-2
+ass	-4
+assassination	-3
+assassinations	-3
+asset	2
+assets	2
+assfucking	-4
+asshole	-4
+astonished	2
+astound	3
+astounded	3
+astounding	3
+astoundingly	3
+astounds	3
+attack	-1
+attacked	-1
+attacking	-1
+attacks	-1
+attract	1
+attracted	1
+attracting	2
+attraction	2
+attractions	2
+attracts	1
+audacious	3
+authority	1
+avert	-1
+averted	-1
+averts	-1
+avid	2
+avoid	-1
+avoided	-1
+avoids	-1
+await	-1
+awaited	-1
+awaits	-1
+award	3
+awarded	3
+awards	3
+awesome	4
+awful	-3
+awkward	-2
+axe	-1
+axed	-1
+backed	1
+backing	2
+backs	1
+bad	-3
+badass	-3
+badly	-3
+bailout	-2
+bamboozle	-2
+bamboozled	-2
+bamboozles	-2
+ban	-2
+banish	-1
+bankrupt	-3
+bankster	-3
+banned	-2
+bargain	2
+barrier	-2
+bastard	-5
+bastards	-5
+battle	-1
+battles	-1
+beaten	-2
+beatific	3
+beating	-1
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+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
index 0449095583a04..030ee30b93381 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
@@ -85,11 +85,8 @@ public Boolean call(String word) throws Exception {
     });
 
     // Read in the word-sentiment list and create a static RDD from it
-    String wordSentimentURI =
-      "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt";
-    String[] wordSentimentLines = IOUtils.toString(URI.create(wordSentimentURI)).split("\n");
-    final JavaPairRDD<String, Double> wordSentiments = jssc.sparkContext().parallelize(
-      Arrays.asList(wordSentimentLines))
+    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) {
diff --git a/examples/src/main/python/streaming/network_wordjoinsentiments.py b/examples/src/main/python/streaming/network_wordjoinsentiments.py
index 385fd4a48f53e..b85517dfdd913 100644
--- a/examples/src/main/python/streaming/network_wordjoinsentiments.py
+++ b/examples/src/main/python/streaming/network_wordjoinsentiments.py
@@ -33,7 +33,6 @@
 from __future__ import print_function
 
 import sys
-import urllib2
 
 from pyspark import SparkContext
 from pyspark.streaming import StreamingContext
@@ -54,10 +53,8 @@ def print_happiest_words(rdd):
     ssc = StreamingContext(sc, 5)
 
     # Read in the word-sentiment list and create a static RDD from it
-    word_sentiments_uri = "http://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/" + \
-                          "AFINN-111.txt"
-    word_sentiments_lines = urllib2.urlopen(word_sentiments_uri).read().split("\n")
-    word_sentiments = ssc.sparkContext.parallelize(word_sentiments_lines) \
+    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]))
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
index 61303ec9be42d..0328fa81ea897 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
@@ -18,7 +18,7 @@
 // scalastyle:off println
 package org.apache.spark.examples.streaming
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkConf
 import org.apache.spark.streaming.twitter.TwitterUtils
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 
@@ -53,10 +53,8 @@ object TwitterHashTagJoinSentiments {
     val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
 
     // Read in the word-sentiment list and create a static RDD from it
-    val wordSentimentURI =
-      "https://raw.githubusercontent.com/fnielsen/afinn/master/afinn/data/AFINN-111.txt"
-    val wordSentimentLines = scala.io.Source.fromURL(wordSentimentURI).mkString.split("\n")
-    val wordSentiments = ssc.sparkContext.parallelize(wordSentimentLines).map { line =>
+    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()