Implementing sentiment analysis

In the following code snippet, we have implemented sentiment analysis based on the NLP theory we discussed in this chapter. It uses SPARK libraries on Tweeter JSON records to train models for identifying sentiments like happy or unhappy. It looks for keywords like happy in the twitter messages and then flags it with value 1 indicating that this message represents a happy sentiment. Other messages are flagged with value 0 which represents unhappy sentiment. Finally TF-IDF algorithm is applied to train models: 

import org.apache.spark.ml.feature.{HashingTF, RegexTokenizer, StopWordsRemover, IDF}
import org.apache.spark.sql.functions._
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import scala.util.{Success, Try}
import sqlContext.implicits._

val sqlContext = new org...