Text Sentiment Analysis In Hadoop & Spark

The source code developed and used for the purposes of my thesis with the same title under the guidance of my supervisor professor Vasilis Mamalis for the Department of Informatics and Computer Engineering of the University of West Attica.

You can read the text and presentation of the thesis (both in greek) and cite it with the URIs below from the university's institutional repository):

• https://polynoe.lib.uniwa.gr/xmlui/handle/11400/395

• http://dx.doi.org/10.26265/polynoe-246

BY-NC-SA – Attribution-NonCommercial-ShareAlike

Main Objectives

• studying the basics of text mining, sentiment analysis, MapReduce, and parallel/distributed computing
• developing a number of applications for sentiment analysis using the Apache Hadoop and Apache Spark frameworks in a cluster of computers using Hadoop as infrastructure
• analyzing the results both in classification (accuracy, F1 measure) from the confusion matrix) and parallel execution (execution time, scalability, speedup)
• coming to conclusions for each application's performance and proposing possible extensions to be made in the future

Developed Applications

All the classifications models of the applications below use 75% of the input for training and the rest 25% for testing purposes.

Using the Hadoop framework

• Simple Version of Naive Bayes
• Modified Version of Naive Bayes (using TFIDF to implement feature selection by only using the top 75% most relevant features)

  Using the Spark framework

• Simple Version of Naive Bayes
• Modified Version of Naive Bayes (using TFIDF to implement feature selection by only using features that are shown in 5 or more documents)
• Simple Version of SVM

• Modified Version of SVM (using TFIDF to implement feature selection by only using features that are shown in 5 or more documents)

  Input Data Used

  To train and test the classification models of each application, this dataset of 1.6 million tweets was used in csv format with each line representing a document/tweet with its unique ID, class label, and tweet text as seen below.

For the Hadoop-based applications, 10 datasets that differ in length of tweets (100k up to 1m tweets) have been created and provided here under the /input directory named train# and test# (# being a number 1-10).

For the Spark-based applications, 10 datasets that differ in length of tweets (100k up to 1m tweets) have been created and provided here under the /input directory appropriately named spark_input_# (# being a number 1-10).

Execution Guide

Up to the development of the source code and compiling of the thesis, the most current stable releases were:

• Apache Hadoop 3.3.0
• Apache Spark 3.0.1

Hadoop-based Application Execution Guide

Simple Version of Naive Bayes

javac -classpath "$(yarn classpath)" -d NB_classes NB.java
jar -cvf NB.jar -C NB_classes/ .
hadoop jar NB.jar NB train# test# training_split testing_split

Modified Version of Naive Bayes

javac -classpath "$(yarn classpath)" -d Modified_NB_classes Modified_NB.java
jar -cvf Modified_NB.jar -C Modified_NB_classes/ .
hadoop jar Modified_NB.jar Modified_NB train# test# training_split testing_split

Where train#, test# are the desires datasets to be used from /input and training_split, testing_split are the amounts of chunks to be split in a number of mappers (must be defined as bytes).

Spark-based Application Execution Guide

Simple Version of Naive Bayes

sbt package
spark-submit --master yarn --deploy-mode client ./target/scala-2.12/nb_2.12-0.1.jar #

Modified Version of Naive Bayes

sbt package
spark-submit --master yarn --deploy-mode client ./target/scala-2.12/modified_nb_2.12-0.1.jar #

Simple Version of SVM

sbt package
spark-submit --master yarn --deploy-mode client ./target/scala-2.12/svm_2.12-0.1.jar #

Modified Version of SVM

sbt package
spark-submit --master yarn --deploy-mode client ./target/scala-2.12/modified_svm_2.12-0.1.jar #

Where # is the number indicating the desired dataset to be used from /input.