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Text Analytics and Sentiment Analysis – an Introduction

Text analytics usually refers to the process of structuring the unstructured text for parsing, indexing, storing, and retrieving, deriving patterns within the structured data, and finally evaluation and interpretation of the output. Typical text analytics tasks include text categorization, text clustering, entity extraction, document summarization, opinion mining, and sentiment analysis. Text analysis involves information retrieval, lexical analysis, and data mining to study word frequency distributions, link and association analysis, visualization, and predictive analytics.
Sentiment analysis refers to the use of natural language processing and text analysis to identify and extract subjective information in source materials. A basic task in sentiment analysis is to classify the polarity of a given text — whether the expressed opinion in the text is positive, negative, or neutral. With the wide use of social media, sentiment analysis becomes a very important topic for research and industry to identify the collective opinion and public response.

In this course, we will introduce the basic concepts and techniques of text mining and sentiment analysis alongside the hands-on practice on several applicable R packages for analysing sentiment. In particular, we will introduce the use of “twitteR” package of document filtering and for preparing for sentiment analysis such as how to remove stop words, punctuation, numbers, html links, and demonstrate how to perform sentiment analysis by “sentiment” and “sentiment140” packages. At last, we also compare the advantages of different methods and how to choose the proper tools for applying in the real applications.

This program is particularly useful for
This program is particularly useful for all those involved or interested in marketing analytics for their organisation:

Industry Practitioners
Students, Researchers, Academics
Program topics

Classical IR Models: Boolean, Vector Space, Probabilistic

Text Processing
Indexing
Storing
Retrieval
Web IR Link Topology: Page Rank, Hubs and Authorities

Basic concepts of sentiment analysis and social media

The use of R packages in sentiment analysis
How to extract twitter data by R programming language
How to perform twitter filtering by text analysis
Different strategies for sentiment analysis in social media and their comparison
Course outcomes
Upon completion of this course students will:

The attendees will gain the basic understanding of sentiment analysis, text analysis and social media
The attendees will understand the common techniques in text analysis
The attendees will know the use of R language to conduct text extraction, preparation and sentiment analysis in social media
Presenters
Shlomo Berkovsky
Dr Guandong Xu
Dr. Sam Ferguson

Shlomo Berkovsky is a Senior Researcher at the CSIRO Computational Informatics division. His broad research interests include user modelling and recommender systems as a means to overcome information overloading. He is interested in collaborative and content-based recommender systems, mediation of user models, ubiquitous user modelling, context-aware personalisation, personalised persuasion, privacy-enhanced personalisation, and tailored information delivery. His prior research interests also include text and web mining, semantic web, social media, and peer-to-peer computing.

Dr Guandong Xu is a senior lecturer in the Advanced Analytics Institute at University of Technology Sydney. He received MSc and BSc degree in Computer Science and Engineering from Zhejiang University, China. He gained PhD degree in Computer Science from Victoria University. After that he took various positions, e.g., Postdoctoral research fellow and Vice-Chancellor Postdoctoral Fellow in the Centre for Applied Informatics at Victoria University, Australia, and Research Assistant Professor in Department of Computer Science at Aalborg University, Denmark. He is an Endeavour Postdoctoral Research Fellow in the University of Tokyo in 2008. He has taught several subjects and short courses to undergraduate/postgraduate students, and the public audience as well.

Dr. Sam Ferguson is a Lecturer in the School of Software, Faculty of Engineering and IT at the University of Technology, Sydney. He has used R in a many of his approximately 35 publications in areas as diverse as spatial hearing and loudness research, to sonification, emotion, and tabletop computing. He has 10 years’ experience teaching programming and technical topics, and has taught numerous subjects at the postgraduate and undergraduate level at the University of Technology, Sydney, the University of Sydney and at UWS. He has been a research fellow or assistant on more than 6 ARC research projects (many of which used R as their primary data analysis language).

Prerequisites: Pre-reading

Book: Modern information retrieval

Sentiment analysis – Wikipedia, the free encyclopedia

Tutorial: Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

Sentiment- Mining Twitter with R

Additional Information

Assessment within Short Course
Post Short Course follow up and engagement
Survey and feedback process
Online readings
Fees

Standard 1 day $800 (GST free)

UTS Alumni and staff- 10% discount
2 or more from the one organisation – 15% discount
Interstate participant – 25% discount
Please select discount option with drop down box before entering your credit card details. Only 1 discount applicable per person.

Group bookings must be made individually with the 15% discount selected and credit card processed separately for each person.

Contact Information
For specific queries regarding course content, please contact Colin Wise, Advanced Analytics Institute Tel: +61 (02) 9514 9267 or email colin.wise@uts.edu.au with questions relating to this course.

When

31st July 2015 09:00 am
To 31st July 2015 05:00 pm

Where

Sydney
235 Jones Street, Ultimo, NSW 2007
Sydney 2007

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