Presentation for the document

1. Web Opinion Mining
Marc-Antoine Dupré
Alexander Patronas
Erhard Dinhobl
Ksenija Ivekovic
Martin Trenkwalder

2. Roadmap
What is opinion mining and why?
Objects, model and task
Words and phrases
Sentiment classification
Feature-based opinion mining
Opinion Spam
Tools on opinion mining

3. Questions:
What do users think about a specific product?
Which of our customers are unsatisfied? Why?
Which product is more popular among users?
Answer: Web Opinion Mining

4. Web Opinion Mining
Facebook, blogs, … > opinion
Wikipedia > fact
Opinions: underlying question
“ what do people in America think about Barack Obama?”
Mostly in deep web
AI algorithm necessary
Useful: market intelligence (better ads)

5. Objects, Model
Opinion holder / object / opinion
Features of object
F = {f1, f2, f3, …} fi ϵ F
fi defined by words or phrases
W = {w1, w2, w3, …} Wi ϵ W
O is some object (event, person, product, …)
“Now the opinion holder is j and comments on a subset of features S j of F of O.
Now feature fk ϵ Sj is commented by j by a word or phrase from Wk to
determine the feature and a positive, negative or neutral opinion on fk”

6. Task
 One document – one opinion from one holder
 Opinion: positive, negative, neutral
 3 levels:
 Document - class determining
 Sentence (one opinion)
 sentence type (objective or subjective)
 sentence class (neutral, positive, negative)
 Feature – determining words and phrases

7. Words and Phrases
Words often context dependent („long“ – long loading time
– long battery runtime)
3 approaches to get wordlist:
Manual approach
Corpus-based approach
Dictionary-based approach

8. Sentiment Classification
Classify documents (e.g. reviews) based on overall
sentiments expressed by opinion holders
Positive, negative or neutral
 Useful, but doesn’t find what reviewer liked or disliked!
A negative sentiment on an object doesn’t mean that opinion
holder dislikes everything about object and opposite
Need to go to sentence level and the feature level

9. Feature-based Opinion Mining
Objective: find what reviewers like and dislike
Features and components
Three tasks:
Extract object features that have been commented on in each
review
Determine whether opinions on the feature are positive,
negative or neutral
Group synonyms and produce summary

10. Different Review Formats
GREAT Camera., Jun 3, 2004
Reviewer: jprice174 from Atlanta, Ga.
I did a lot of research last year before I
bought this camera... It kinda hurt to leave behind
my beloved nikon 35mm SLR, but I was going to
Italy, and I needed something smaller, and digital.
The pictures coming out of this camera are
amazing. The 'auto' feature takes great pictures
most of the time. And with digital, you're not
wasting film if the picture doesn't come out. …
….

11. Extracting Object Features
1. Part-of-speech tagging:
 Features are noun and noun phrases
2. Frequent features generation
 Association mining to generate candidate features
 Feature pruning
3. Infrequent feature generation
 Opinion words extraction
 Finding infrequent features using opinion words

12. Identifying Orientation of Opinion
Sentence
Used dominant orientation of opinion words as sentence
orientation
If positive opinion prevails, the opinion sentence is regarded as
a positive and vice versa

13. Feature-based Summary
GREAT Camera., Jun 3, 2004 Feature Based Summary:
Reviewer: jprice174 from Atlanta, Ga.
Feature1: picture
I did a lot of research last year before
Positive: 12
I bought this camera... It kinda hurt to
 The pictures coming out of this camera are
leave behind my beloved nikon 35mm amazing.
SLR, but I was going to Italy, and I needed  Overall this is a good camera with a really good
something smaller, and digital. picture clarity.
The pictures coming out of this …
camera are amazing. The 'auto' feature Negative: 2
takes great pictures most of the time. And  The pictures come out hazy if your hands shake
with digital, you're not wasting film if the even for a moment during the entire process of
picture doesn't come out. … taking a picture.
 Focusing on a display rack about 20 feet away in
a brightly lit room during day time, pictures
…. produced by this camera were blurry and in a
shade of orange.
Feature2: battery life
…

14. Opinion Spam
Reviews contain rich user opinions on products and services,
that possibly influence the purchase decisions of users
Generally three types of spam reviews:
Untruthful opinions
Reviews on brands only
Non-Reviews

15. Tools for Sentiment Analysis [1/2]
APIs
Evri – semantic search engine, very powerful API
OpenDover – Java based webservice
Blogosphere/Twittersphere
RankSpeed – search by criterias
Twittratr – simple search tool (keyword based)
TwitterSentiment – project from Stanford University,
classifiers from machine learning algorithms, transparent

16. Tools for Sentiment Analysis [2/2]
Newspaper
Newssift – sentiment search tool on newspapers (by Financial
Times)
Applications
LingPipe – Java tool
Radian6 – commercial social media monitoring application
RapidMiner – open-source machine learning and data mining
tool (Community Edition)

22. LIVE DEMO (evri)

23. Thank you
for your attention!