14 December 2011 in Kassel, Germany

1. Sentiment Mining
Prof. Maurice Mulvenna
University of Kassel
14 December 2011

2. Outline
§ Ulster
§ What is Sentiment Mining
§ Why Sentiment Mining
§ Challenges
§ Methods
§ Data Sources
§ Applications
§ Examples
§ Simple Keyword-based Prototype
§ Some Results

3. The Right Choice
COLERAINE JORDANSTOWN
MAGEE BELFAST
FOUR CAMPUSES-ONE UNIVERSITY

4. • Largest University in Ireland – over 25,500 local,
national and international student body
• International reputation in research
• “Excellence” in teaching
• Graduate employment well above national average
• Excellent study and recreational facilities
University of Ulster
in Top 10 UK universities in applications

5. What to Study
Computing and Multimedia
Electrical and Mechanical Engineering
Humanities/Performing Arts
Life and Health Sciences
Social Sciences
Art, Design and Built Environment
Business and Management
Around 600 degree programmes:

6. Faculty of Computing and Engineering
Within the Faculty there are:
§ 5 Schools
§ Approximately 3000 students
§ 200 staff
§ Extensive specialist facilities on the Coleraine,
Jordanstown and Magee Campuses

9. What is Sentiment Mining
§ Also referred to as sentiment analysis or opinion
mining
§ It refers to the application of natural language
processing, computational linguistics, and text
analytics to identify and extract subjective
information in source materials. (Wikipedia)
§ Its aim is to determine
the attitude or mood of a user or user group (i.e. happy or sad)
the contextual polarity of statements or larger documents
(i.e. positive or negative)
the intended emotional communication (i.e. sarcasm or irony)

10. Why Sentiment Mining
§ Capture and analyse public opinion
§ Capturing the word-of-mouth effect
§ Evaluate the social profile of individual
§ News detection and analysis
§ Quantify the emotional state of users (i.e. duress,
stress, sadness, angriness, etc.)
§ Feedback mechanism to e.g. policy makers
§ National (e.g., UK riots) and
§ International ( ‫ﻝلﺭرﺏبﻱيﻉع‬‫ﺍاﻝلﻉعﺭرﺏبﻱي‬ ‎ or ‘Arab Spring’)
events that impact and resonate in peoples’ daily
lives

11. Marketing
http://mashable.com/2011/11/23/kindle-fire-nook-ipad-online-buzz/

12. Challenges
§ Sentiment is a subjective measure and as such is subject
to interpretation
§ Data Volumes
Number of statements, users, documents, etc.
Size of documents and the complexity (topic, sentence,
paragraph, chapter, document level)
§ Noise, and unstructured data
§ Slang, vernaculars, abbreviations (i.e. wdc, cu, ru, lol, etc.)
§ Language heterogeneity
Demographic dependencies
Social dependency
§ Ambivalence
§ Complexity of NLP tasks

13. Methods
§ Keyword-based approaches
§ Machine learning techniques
Latent semantic analysis
Support vector machines
"bag of words” Methods
Naive Bayes classifiers
Other NLP tools that allow the detailed parsing
of text related sources including the underlying
grammar.

14. Data Sources
§ Any single document or document collection (i.e.
reviews of any kind – travel, food, movie, etc.)
§ Social media networks (i.e. Twitter)
§ Spoken communication (either directly or after
converting it into a textual representation)
à Any source in which an opinion or emotion is
expressed or communicated

15. Applications
§ Reputation Management
§ Customer Profiling
§ Product Management
§ News Detection and Analysis
§ Public Opinion Analysis
§ Affective Computing where systems should
interpret the emotional state of users and adapt
there behaviour accordingly also providing an
appropriate response for the emotions detected.

17. The essence of the book
is Lanier's attempt to
answer the question:
"What happens when we
stop shaping technology
and technology starts
shaping us?" "

18. Prototype Architecture

19. Prototype Interface

20. Topic Map & Tag Clouds

21. Timeline

22. Runtime

23. Keyword
100 Keywords
6500 Keywords

24. Evaluation & Accuracy
Manual Classification
SentiGen

28. Thank you