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August 24, 2020

use the developed engine to predict hotel reviewers’ sentiments and benchmark various algorithms and analytics tools.

Case Study A: you will work as a social marketing analyst in a consulting company to uncover the impacts of online advertising and communication with customers. The aim of the study is to educate the marketing teams of their clients (in diverse industries) to market their products and/or services on social media to maximise customers’ involvement (positive interest and sharing). The company is interested in finding out the relationship between the keywords, shares, sentiments and whether there is a relationship in different topic categories such as entertainment, technology, business, etc. that are of interest to different clients in various industries. 2. Case Study B: you will be a data scientist working for a hotel review firm 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 SAS Sentiment Analysis Studio (covered in the workshop activities on Week 3 and Week 4). You are required to use the developed engine to predict hotel reviewers’ sentiments and benchmark various algorithms and analytics tools.

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