How to implement pipeline architecture for sentiment analysis in spark with R ?

Description

To implement pipeline architecture for sentiment analysis in spark with R using sparklyR package

Functions used :

 spark_connect(master = “local”) – To create a spark connection
 sdf_copy_to(spark_connection,R object,name) – To copy data to spark environment
 sdf_partition(spark_dataframe,partitions with weights,seed) – To partition spark dataframe into multiple groups
 ml_pipeline(spark connection) – To create a Spark ML pipeline
 ft_r_formula(formula) – To implement the transforms required for fititng a dataset against an R model formula
 ml_random_forest_classifier() – To build a random forest model
 ml_fit(pipeline_model,train_data) – To fit the model
 ml_transform(fitted_pipeline,test_data) – To predict the test data

  • Load the sparklyr library
  • Create a spark connection
  • Copy data to spark environment
  • Split the data for train and test
  • Create an empty pipeline model
  • Fit the pipeline model using the train data
  • Predict using the test data
  • Evaluate the metrics

#Load the sparklyr library
library(sparklyr)
#Create a spark connection
sc #Copy data to spark environment
data_amz %
ft_tokenizer(input_col=”V1″,output_col=”Tokenized”) %>%
ft_stop_words_remover(input_col=”Tokenized”,output_col =”Stp_rmvd”)%>%
ft_hashing_tf(input_col = “Stp_rmvd”,output_col = “Hash”)%>%
ft_idf(input_col=”Hash”,output_col=”IDF”)%>%
ft_r_formula(V2~IDF) %>%
ml_random_forest_classifier()
#Split the data for train and test
partitions=sdf_partition(data_amz,training=0.8,test=0.2,seed=111)
train_data=partitions$training
test_data=partitions$test
#Fit the pipeline model
fitted_pipeline fitted_pipeline
#Predict using the test data
predictions predictions
#Evaluate the metrics AUC
cat(“Area Under Curve : “,ml_binary_classification_evaluator(predictions, label_col = “label”,prediction_col = “prediction”))

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