BUS5WB - Data Warehousing and Big Data
Assignment 03: Big Data Analytics
Marks: 30%
Assignment Type: Individual
Release Date: Thursday 3rd May 2018
Due Date: Sunday 3rd June 2018
The third assignment focuses on Big Data analytics on unstructured text data using Microsoft Azure. You are required to derive insights by applying big data distributed processing and machine learning techniques.
What you are required to do
1. HDInsight to aggregate reviews
Develop an aggregate of these reviews using your knowledge of Hadoop and MapReduce in Microsoft HDInsight.
a) Follow the same approach as the Big Data analytics workshop (using wordcount method in HDInsight) to determine the contributory words for each level of rating and sentiment category.
b) Present the workflow of using HDInsight (you may use screen captures) along with a summary o findings for each level of rating and sentiment category. MapReduce documentation for HDInsight is available here.
IMPORTANT: STUDENT ACCOUNTS HAVE LIMITED AZURE CREDITS. YOU MUST CREATE AND DECOMMISSION (DELETE) THE HDINSIGHT CLUSTERS EACH TIME YOU ATTEMPT THE ASSIGNMENT. IF YOU ARE PLANNING TO WORK ON THE ASSIGNMENT ACROSS MULTIPLE DAYS, REMEMBER TO DELETE AND RECREATE EACH TIME.
2. Azure Machine Learning for sentiment analysis
Using Azure ML Studio to cluster user reviews based on sentiment score. For text clustering, you should use the ‘review’ field. In Filter based feature selection module, use ‘sentiment’ field in order to cluster reviews based on sentiment score. Download the cluster outputs into a csv file to interpret the results and derive insights. You will need to calibrate algorithmic parameters by using different Number of Centroids and Distance Metric to derive meaningful clusters. Exclude sentiment, rating or postid as selected columns to train the clustering model. Use only the preprocessed hashing features.
Provide the following,
a) A screen capture of the completed model diagram.
b) Details of parameters used for 1) feature hashing module, 2) filter based feature selection module and 3) K-Means clustering module
c) Details of the approach you chose for clustering and interpretation of clusters.
3. Findings
Summarise your findings from 1) and 2), on user rating, hotel rating and sentiment towards accommodation options in Vietnam. Consider the challenges you faced in conducting Big Data analytics on a real-life text dataset.
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1. HDInsight for Aggregated Reviews:
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