Part of the Search Engine course given in the Technion (2011)

1. Mining Explicit User Generated Content:
Sentiment Analysis
Kira Radinsky
Slides based on material from: Bing Liu (WWW-2008 tutorial)

2. 2
Introduction
• Two main types of textual information.
– Facts and Opinions
• Most current text information processing
methods (e.g., web search, text mining) work
with factual information.
• Sentiment analysis/opinion mining
– computational study of opinions, sentiments and
emotions expressed in text.
– huge volumes of opinionated text on the web

3. 3
User Generated Media
Word-of-mouth on the Web
– User-generated media: One can express opinions
on anything in reviews, forums, discussion groups,
blogs ...
– Opinions of global scale: No longer limited to:
• Individuals: one’s circle of friends
• Businesses: Small scale surveys, tiny focus
groups, etc.

4. An Example Review
• “I bought an iPhone a few days ago. It was such a nice
phone. The touch screen was really cool. The voice quality
was clear too. Although the battery life was not long, that
is ok for me. However, my mother was mad with me as I
did not tell her before I bought the phone. She also thought
the phone was too expensive, and wanted me to return it
to the shop. …”
• What do we see?
– Opinions, targets of opinions, and opinion holders
4

5. 5
Target Object
• Definition (object): An object o is a product, person, event,
organization, or topic. o is represented as
– A hierarchy of components, sub-components, and so on.
– Each node represents a component and is associated with a
set of attributes of the component.
• An opinion can be expressed on any node or attribute of
the node (also called features)
* Liu, Web Data Mining book, 2006

6. What is an Opinion?
An opinion :
(oj, fjk, soijkl, hi, tl),
• oj is a target object.
• fjk is a feature of the object oj.
• hi is an opinion holder.
• tl is the time when the opinion is expressed.
• soijkl is the sentiment value of the opinion of
the opinion holder hi on feature fjk of object oj
at time tl. soijkl is +ve, -ve, or neu, or a more
granular rating. 6

7. Sentiment Analysis approaches
• Document level sentiment classification
– Unsupervised review classification (Turney, ACL-02)
– Sentiment classification using machine learning
methods (Pang et al, EMNLP-02)
• Sentence level sentiment analysis
– Using learnt patterns (Rilloff and Wiebe, EMNLP-03)
• Feature-based opinion mining and
summarization
– Next slides

8. 8
Feature-Based Sentiment Analysis
• Objective: Discovering all quintuples
(oj, fjk, soijkl, hi, tl)
• Sentiment classification at both document and sentence
(or clause) levels are not enough,
– they do not tell what people like and/or dislike
– A positive opinion on an object does not mean that the opinion
holder likes everything.
– An negative opinion on an object does not mean that the opinion
holder dislikes everything.

9. 9
Feature-Based Opinion Summary
“I bought an iPhone a few days
ago. It was such a nice phone.
The touch screen was really cool.
The voice quality was clear too.
Although the battery life was not
long, that is ok for me. However,
my mother was mad with me as
I did not tell her before I bought
the phone. She also thought the
phone was too expensive, and
wanted me to return it to the
shop. …”
Feature Based Summary:
Feature1: Touch screen
Positive: 212
• The touch screen was really cool.
• The touch screen was so easy to use
and can do amazing things.
…
Negative: 6
• The screen is easily scratched.
• I have a lot of difficulty in removing
finger marks from the touch screen.
…
Feature2: battery life
…
*Hu & Liu, KDD-2004

10. 10
Visual Comparison
 Summary of
reviews of
Cell Phone 1
Voice Screen Size WeightBattery
+
_
 Comparison of
reviews of
Cell Phone 1
Cell Phone 2
_
+
* Liu et al. WWW-2005

11. Bing feature-based opinion summary
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12. Sentiment Analysis is Hard!
• “This past Saturday, I bought a Nokia phone
and my girlfriend bought a Motorola phone
with Bluetooth. We called each other when we
got home. The voice on my phone was not so
clear, worse than my previous phone. The
battery life was long. My girlfriend was quite
happy with her phone. I wanted a phone with
good sound quality. So my purchase was a real
disappointment. I returned the phone
yesterday.”
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13. Not Just ONE Problem
• (oj, fjk, soijkl, hi, tl),
– oj - a target object: Named Entity Extraction (more)
– fjk - a feature of oj: Information Extraction
– soijkl is sentiment: Sentiment determination
– hi is an opinion holder: Information/Data Extraction
– tl is the time: Data Extraction
• Co-reference resolution
• Relation extraction
• Synonym match (voice = sound quality) …
• None of them is a solved problem!
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14. Easier and Harder Problems
• Reviews are easier.
– Objects/entities are given (almost), and little noise
• Forum discussions and blogs are harder.
– Objects are not given, and a large amount of noise
• Determining sentiments seems to be easier.
• Determining objects and their corresponding
features is harder.
• Combining them is even harder.
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15. 15
Two Main Types of Opinions
• Direct Opinions: direct sentiment expressions
on some target objects, e.g., products, events,
topics, persons.
– E.g., “the picture quality of this camera is great.”
• Comparative Opinions: Comparisons
expressing similarities or differences of more
than one object. Usually stating an ordering or
preference.
– E.g., “car x is cheaper than car y.”

16. 16
Comparative Opinions
• Gradable
– Non-Equal Gradable: Relations of the type greater
or less than
• Ex: “optics of camera A is better than that of camera B”
– Equative: Relations of the type equal to
• Ex: “camera A and camera B both come in 7MP”
– Superlative: Relations of the type greater or less
than all others
• Ex: “camera A is the cheapest camera available in
market”
* Jindal and Liu, AAAI 2006

17. Mining Comparative Opinions
(Jinal and Liu, SIGIR-06)
Given a collection of evaluative texts
Task 1: Identify comparative sentences.
Task 2: Categorize different types of
comparative sentences.
Task 2: Extract comparative relations from the
sentences.

18. Identify comparative sentences
Keyword strategy
• An observation: It is easy to find a small set of
keywords that covers almost all comparative sentences, i.e.,
with a very high recall and a reasonable precision
• Compiled a list of 83 keywords used in comparative
sentences, which includes:
– Words with POS tags of JJR, JJS, RBR, RBS
• POS tags are used as keyword instead of individual
words.
• Exceptions: more, less, most and least
– Other indicative words like beat, exceed, ahead, etc
– Phrases like in the lead on par with etc

19. 2-step learning strategy
• Step1: Extract sentences which contain at
least a keyword (recall = 98%, precision =32%
on our data set for gradables)
• Step2: Use the naïve Bayes (NB) classifier to
classify sentences into two classes:
comparative and non-comparative, and use
features like:
– Use words within radius r of a keyword to form a
sequence (words are replaced with POS tags)
– Use different minimum supports for different
keywords (multiple minimum supports)

20. Mining Comparative Opinions
1. (Bos and Nissim 2006) proposes a method to
extract items from superlative sentences. It
does not study sentiments either.
2. (Fiszman et al 2007) tried to identify which
entity has more of a certain property in a
comparative sentence.
3. (Ding and Liu 2008) studies sentiment
analysis of comparatives, i.e., identifying
which entity is preferred.
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