During presidential elections in the United States, it has become customary for the main candidates (almost always the candidates of the two largest parties, currently the Democratic Party and the Republican Party) to engage in a debate. The topics discussed in the debate are often the most controversial issues of the time, and arguably elections have been nearly decided by these debates (e.g., Nixon vs. Kennedy). Here in this project we have analyzed the Presidential debates of Barack Obama and Mitt Romney for 2012 U.S. presidential general election.

1. Does the Presidential debates have any
impact on election results?
Ashwath V
Avudaiappan R
Harish A
Sudhakaran S
Suganthy E
Monday, January 18, 2016 Active Learning Project 1

2. Big Data (Hadoop - Map Reduce, Pig, Hive) – for input text file processing
R & Tableau – for Visualization
Excel – for output interpretation
Tools and Technologies used
Reference
http://www.debates.org/index.php?page=debate-transcripts
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3. Monday, January 18, 2016 3

4. Does the Presidential debates have any impact on
election results?
Objective:
• Finding out the word frequency and distribution for different parts of speech.
• Categorization of Positive and Negative words from the speech and if it has any impact
on election results
Our analysis will be helpful, If you are any of the
following:
• Do you want to contest in the next Presidential Elections?
• Would you like to know which of the two candidates fared well after the debate?
• Are you a political or a media person?
Sources:
www.debates.org
Well, my first job as
commander in chief, is to keep
the American people safe.
We need strong leadership. I'd
like to be that leader with your
support. I'll work with you.
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5. MAPPER -Code to find word count
/**The mapper reads one line at the time, splits it into an array of single words and emits every word to
the reducers with the value of 1**/
public static class TopNMapper extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private String tokens = "[_|$#<>^=[]*/,;,.-:()?!"']";
@Override
public void map(Object key, Text value, Context context) throws IOException, InterruptedException
{
String cleanLine = value.toString().toLowerCase().replaceAll(tokens, " ");
StringTokenizer itr = new StringTokenizer(cleanLine);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken().trim());
context.write(word, one);
}}}
MAP-REDUCE
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6. REDUCER -Code to find word count
/** The reducer retrieves every word and puts it into a Map: if the word already exists in the
map, increments its value, otherwise sets it to 1**/
public static class TopNReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private Map<Text, IntWritable> countMap = new HashMap<>();
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
// **computes the number of occurrences of a single word**/
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
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7. OUTPUT
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8. Code to find word count
/**Loading data**/
debate_dataset = LOAD '/home/cloudera/Desktop/debate.txt' AS (line:chararray);
/** Extract words from each line and put them into a pig bag**/
/** datatype, then flatten the bag to get one word on each row**/
words = FOREACH input_lines GENERATE FLATTEN(TOKENIZE(line)) AS word;
/**filter out any words that are just white spaces**/
filtered_words = FILTER words BY word MATCHES 'w+';
/**create a group for each word**/
word_groups = GROUP filtered_words BY word;
/**count the entries in each group**/
word_count = FOREACH word_groups GENERATE COUNT(filtered_words) AS count,
group AS word;
/**order the records by count**/
ordered_word_count = ORDER word_count BY count DESC;
STORE ordered_word_count INTO '/home/cloudera/Desktop/wordcountoutput’;
PIG
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9. OUTPUT
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10. I wonder who spoke more
positively?? How to find it??
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11. Monday, January 18, 2016 Active Learning Project 11

12. Map Reduce code
public class CompareTwoFiles {
public static class Map extends
Mapper<LongWritable, Text, LongWritable, Text> {
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
context.write(key, value);
}
} public static class Map2 extends
Mapper<LongWritable, Text, LongWritable, Text> {
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
context.write(key, value);
}
}
public static class Reduce extends
Reducer<LongWritable, Text, LongWritable, Text> {
@Override
public void reduce(LongWritable key, Iterable<Text> values,
Context context) throws IOException, InterruptedException
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13. Map Reduce code
{
String[] lines = new String[2];
int i = 0;
for (Text text : values) { lines[i] = text.toString(); i++; }
if (lines[0].equals(lines[1])) { context.write(key, new Text("same")); }
else { context.write(key, new Text(lines[0] + " vs " + lines[1])); } } }
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.set("fs.default.name", "hdfs://localhost:8020"); Job job = new Job(conf);
job.setJarByClass(CompareTwoFiles.class);
job.setJobName("Compare Two Files and Identify the Difference");
FileOutputFormat.setOutputPath(job, new Path(args[2]));
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(LongWritable.class);
job.setOutputValueClass(Text.class);
MultipleInputs.addInputPath(job, new Path(args[0]), TextInputFormat.class, Map.class);
MultipleInputs.addInputPath(job, new Path(args[1]), TextInputFormat.class, Map2.class);
job.waitForCompletion(true);
}
}
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14. Positive words Output
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15. Obama Romney
Series1 194 153
194
153
0
50
100
150
200
250POSITIVEWORDCOUNT
Obama VS Romney
Final Interpretation
Our Analysis reveals that positive words spoken by Barack Obama is comparatively
higher than Mitt Romney. Coincidentally Obama was elected as the President of
United States of America.
Yes, debates do have an impact on the Election results !
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16. Positive words cloud
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17. THANK YOU
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