BUS5CA - Customer Analytics And Social Media - Sentiment Analysis & Customer Profiling Case Study - Case Study Assignment Help

Assignment Task -                 
 

1. Case Study A (Sentiment Analysis): As a data scientist working for a movie review firm, you are tasked to develop a sentiment analytics engine for Twitter, which is used to predict consumers’ review sentiments. The aim is to develop both dictionary-based and machine learning-based sentiment analytics scripts using a number of R  libraries and the SAS Sentiment Analysis Studio (which were covered in the workshop activities on Week 3 and Week 4 in Semester 2). You are required to use the developed engine to predict movie reviewers’ sentiments and benchmark various algorithms and analytics tools.  

2. Case Study B (Customer Profiling): Customer segmentation is the process of splitting customers into different groups with similar characteristics for the potential business value proposition. Many companies find that segmenting their customers enables them to communicate, engage with their customers more effectively. Moore Bank is conducting an analysis on the existing customer profiles and the marketing campaign data to identify the target customers who are most likely to subscribe to long-term deposits. As a member of the data analytics team, you are tasked to analyze historical data and develop predictive models for marketing purposes. Your manager has designed a pilot project focusing on clustering-based customer segmentation and profiling to discover consumer insights.

Case Study A (15%) 

Sentiment analysis is the technique aiming to gauge the attitudes of customers in relation to topics, products, and services of interest. It is a pivotal technology for providing insights to enhance the business bottom line in campaign tracking, customer-centric marketing strategy, and brand awareness. Sentiment analytics approaches are used to produce sentiment categories such as ‘positive’, ‘negative’, and ‘neutral’. More specific human emotions are also the topic of interest. There are two major streams of methods to develop a sentiment analytics engine: the dictionary-based and machine learning-based approaches. In this part of the assignment, you are required to perform sentiment analytics based on both approaches.

Task Requirements: 

As a data scientist, you are required to perform a number of data analytics tasks. You are tasked to develop both dictionary-based and machine-learning sentiment analytics engines using the R programming language and apply it to predict the sentiments of movie review tweets from a sample of data. You are also required to use the SAS Sentiment Analysis Studio to compare the results. 

To achieve the above, you need to carry out the following data analytics tasks: 

Task 1: Develop a dictionary-based sentiment analytics engine based on the R library  ‘syuzhet’ and ‘tidytext’ to analyze the different emotions from the review tweets (5%). • Analyse and aggregate the eight emotions (anger, anticipation, disgust, fear, joy,  sadness, surprise and trust) from the review tweets file ‘movie_tweets.csv’ using the function ‘get_nrc_sentiment’. (You are required to plot a chart to visualize these emotions using the R library ‘ggplot2’.) 

• Finding the top 5 most frequent words in all the movie reviews for each of the eight emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). Analyze and discuss the results. 

Task 2: Develop a machine learning-based model using the R libraries ‘tm’ and ‘e1071’ as well as evaluate the predictive accuracies of two classifiers (5%). 

• Develop R scripts and import the data sets from the folder ‘movie_tweets’ for training and testing. 

• Use both the negative tweets and the positive tweets from the subfolder ‘training’  as the training dataset, and use the rest of the negative tweets and the positive tweets from the subfolder ‘testing’ as the testing dataset. 

(Hint: You may need to use as. character() function to convert a data frame column  from factors to characters.) 

• Develop a machine learning-based sentiment analytics engine and predict sentiment categories (only as ‘positive’ and ‘negative’) using ‘tm’ and ‘e1071’ with the Naïve Bayes classifier and the SVM classifier. 

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