1. Opinion Mining and Sentiment Analysis
Slides from Bing Liu and Ronan Feldman

2. Introduction Two main types of textual information.
Facts and Opinions
Note: factual statements can imply opinions too.
Most current text information processing methods (e.g., web search, text mining) work with factual information.
Sentiment analysis or opinion mining
computational study of opinions, sentiments and emotions expressed in text.
Why opinion mining now? Mainly because of the Web; huge volumes of opinionated text.

3. Introduction – user-generated media
Importance of opinions:
Opinions are useful when making a decision, we want to hear others’ opinions.
In the past,
Individuals: opinions from friends and family
businesses: surveys, focus groups, consultants …
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. Sentiment analysis applications
Businesses and organizations
Benchmark products and services; market intelligence.
Businesses spend a huge amount of money to find consumer opinions using consultants, surveys and focus groups, etc
Individuals
Make decisions to purchase products or to use services
Find public opinions about political candidates and issues
Ad placement: e.g. in social media
Place an ad if one praises a product.
Place an ad from a competitor if one criticizes a product.
Opinion retrieval: provide general search for opinions.

5. A Fascinating Problem!
Intellectually challenging & major applications.
A popular research topic in recent years in NLP and Web data mining.
20-60 companies in USA alone
It touches every aspect of NLP and yet is restricted and confined.
Little research in NLP/Linguistics in the past.
Potentially a major technology from NLP
But “not yet” and not easy!
Data sourcing and data integration are hard too!

6. Absract Problem Statement
It consists of two parts
Opinion definition
What is an opinion?
Opinion summarization
Opinions are subjective. An opinion from a single person (unless a VIP) is often not sufficient for action.
We need opinions from many people, and thus opinion summarization.

7. 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

8. Entity and aspect/feature level
Id: Abc123 on “I bought an iPhone a few days ago. It is such a nice phone. The touch screen is really cool. The voice quality is clear too. It is much better than my old Blackberry, which was a terrible phone and so difficult to type with its tiny keys. 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 …”
What do we see?
Opinion targets: entities and their features
Sentiments: positive and negative
Opinion holders: persons who hold the opinions
Time: when opinions were expressed

9. Two main types of opinions
Regular opinions: Sentiment/opinion expressions on some target entities
Direct opinions:
“The touch screen is really cool”
Indirect opinions:
“After taking the drug, my pain has gone”
Comparative opinions: Comparisons of more than one entity
E.g., “iPhone is better than Blackberry”
We focus on regular opinions first, and just call them opinions.

10. Entity and Aspect (Liu, Web Data Mining book, 2006)
Definition (entity): An entity e is a product, person, event, organization, or topic. e 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.
For simplicity, we use the term aspects (features) to represent both components and attributes.

11. Opinion definition (Liu, a Ch. in NLP handbook)
An opinion is a quintuple
(oj, fjk, soijkl, hi, tl),
where
oj is a target object.
fjk is a feature of the object oj.
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.
hi is an opinion holder.
tl is the time when the opinion is expressed.

12. Our example in quintuples
(iPhone, GENERAL, +, Abc123, )
(iPhone, touch_screen, +, Abc123, )

13. Alternative terminology
Entity is also called object.
Aspect is also called feature, attribute, facet, etc
Opinion holder is also called opinion source

14. Structure the unstructured
Goal: Given an opinionated document,
Discover all quintuples (oj, fjk, soijkl, hi, tl),
i.e., mine the five corresponding pieces of information in each quintuple, and
Or, solve some simpler problems
E.g. classify the sentiment of the entire document
With the quintuples,
Unstructured Text  Structured Data
Traditional data and visualization tools can be used to slice, dice and visualize the results in all kinds of ways
Enable qualitative and quantitative analysis.

15. Sentiment Classification: doc-level (Pang and Lee, et al 2002 and Turney 2002)
Classify a document (e.g., a review) based on the overall sentiment expressed by opinion holder
Classes: Positive, or negative (and neutral)
In the model, (oj, fjk, soijkl, hi, tl),
It assumes
Each document focuses on a single object and contains opinions from a single opinion holder.
It considers opinion on the object, oj (or oj = fjk)

16. Subjectivity
Sentence subjectivity: An objective sentence presents some factual information, while a subjective sentence expresses some personal opinions, beliefs, views, feelings, or emotions.
Not the same as emotion

17. Subjectivity Analysis (Wiebe et al 2004)
Sentence-level sentiment analysis has two tasks:
Subjectivity classification: Subjective or objective.
Objective: e.g., I bought an iPhone a few days ago.
Subjective: e.g., It is such a nice phone.
Sentiment classification: For subjective sentences or clauses, classify positive or negative.
Positive: It is such a nice phone.
However. (Liu, Chapter in NLP handbook)
subjective sentences ≠ +ve or –ve opinions
E.g., I think he came yesterday.
Objective sentence ≠ no opinion
Imply –ve opinion: My phone broke in the second day.

18. Rational and emotional evaluations
Rational evaluation: Many evaluation/opinion sentences express no emotion
E.g. “The voice on this phone is clear”
Emotional evaluation
E.g. “I love this phone”
“The voice on this phone is crystal clear” (?)
Some emotion sentences express no (positive or negative) opinion/sentiment
E.g. “I am so surprised to see you”

19. Feature-Based Sentiment Analysis
Sentiment classification at both document and sentence (or clause) levels are not sufficient,
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 …..
Objective: Discovering all quintuples
(oj, fjk, soijkl, hi, tl)
With all quintuples, all kinds of analyses become possible.

20. Opinion Summary With a lot of opinions, a summary is necessary.
A multi-document summarization task
For factual texts, summarization is to select the most important facts and present them in a sensible order while avoiding repetition
1 fact = any number of the same fact
But for opinion documents, it is different because opinions have a quantitative side & have targets
1 opinion ≠ a number of opinions
Aspect-based summary is more suitable
Quintuples form the basis for opinion summarization

21. Feature-Based Opinion Summary (Hu & Liu, KDD-2004)
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
Note: We omit opinion holders
“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. …” ….

22. Visual Comparison (Liu et al. WWW-2005)
Summary of reviews of Cell Phone 1
Voice
Screen
Size
Weight
Battery + _
Comparison of reviews of
Cell Phone 1
Cell Phone 2 _ +

23. Aspect-based opinion summary

24. Google Product Search

25. Comparing 3 GPSs on different features
Each bar shows the proportion of +ve opinion

26. Demo 1: Detail opinion sentences
You can click on any bar to see the opinion sentences. Here are negative opinion sentences on the maps feature of Garmin.
The pie chart gives the proportions of opinions.

27. # of feature mentions
People talked more about prices than other features. They are quite positive about price, but not bout maps and software.

28. Aggregate opinion trend
More complains in July - Aug, and in Oct – Dec!

29. Sentiment Analysis is Challenging!
“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.”

30. Senti. Analysis Requires solving several IE problems
(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!

31. Easier and harder problems
Tweets from Twitter are probably the easiest
short and thus usually straight to the point
Stocktwits are much harder! (more on that later)
Reviews are next
entities are given (almost) and there is little noise
Discussions, comments, and blogs are hard.
Multiple entities, comparisons, noisy, sarcasm, etc
Extracting entities and aspects, and determining sentiments/opinions about them are hard.
Combining them is harder.

32. Extraction of competing objects
The user first gives a few objects/products as seeds, e.g., BMW and Ford.
The system then identifies other competing objects from the opinion corpus.
The problem can be tackled with PU learning (Learning from positive and unlabeled examples) (Liu et al 2002, 2003).
See (Li et al. ACL-2010)

33. Feature/Aspect-based sentiment analysis

34. Aspect-based sentiment analysis
Much of the research is based on online reviews
For reviews, aspect-based sentiment analysis is easier because the entity (i.e., product name) is usually known
Reviewers simply express positive and negative opinions on different aspects of the entity.
For blogs, forum discussions, etc., it is harder:
both entity and aspects of entity are unknown,
there may also be many comparisons, and
there is also a lot of irrelevant information.

35. Find entities
Although similar, it is somewhat different from the traditional named entity recognition (NER).
E.g., one wants to study opinions on phones given Motorola and Nokia, find all phone brands and models in a corpus, e.g., Samsung, Moto.

36. Feature/Aspect extraction
Extraction may use:
frequent nouns and noun phrases
Sometimes limited to a set known to be related to the entity of interest or using part discriminators
e.g., for a scanner entity “of scanner”, “scanner has”
opinion and target relations
Proximity or syntactic dependency
Standard IE methods
Rule-based or supervised learning
Often HMMs or CRFs (like standard IE)

37. Double Propagation Proposed in (Qiu et al. IJCAI-2009)
Like co-training
It exploits the dependency relations of opinions and features to extract features.
Opinions words modify object features, e.g.,
“This camera takes great pictures”
The algorithm bootstraps using a set of seed opinion words (no feature input).
To extract features (and also opinion words)

38. This phone has good screen
The DP method
DP is a bootstrapping method
Input: a set of seed opinion words,
no aspect seeds needed
Based on dependency tree (Tesniere 1959)
This phone has good screen

39. Rules from dependency grammar

40. Group synonym features (Zhai et al. 2010)
Features that are domain synonyms should be grouped together.
Many techniques can be used to deal with the problem, e.g.,
Topic modeling, distributional similarity, etc
We proposed a semi-supervised learning method
Z. Zhai, B. Liu, H. Xu and P. Jia. Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints. COLING-2010.

41. Coreference resolution (Ding and Liu 2010)
Different from traditional coreference resolution
Important to resolve objects and features
E.g.., “I bought a Canon S500 camera yesterday. It looked beautiful. I took a few photos last night. They were amazing”.
Some specific characteristics of opinions can be exploited for better accuracy. See
X. Ding and B. Liu, Resolving Object and Attribute Coreference in Opinion Mining. COLING-2010.

42. Coreference Resolution by Parse Analysis and Similarity Clustering
(Attardi et al. 2010) best performing at SemEval 2010
Identifies mentions from dependency trees
Uses eager classifier to cluster mentions
Positive and negative instances are created by pairing each mention with each of the preceding ones
Features extracted from pairs of mentions:
Lexical: Same, Prefix, Suffix, Acronym …
Distance: Edit, mention, token, sentence
Syntax: same HeadPoS, pair of HeadPos
Pair of counts of mention occurrences
Same NE type
For pronouns: type, pair of genders, pair of numbers

43. Accuracy at SemEval 2010 Mention CEAF B3 Catalan 82.7 57.1 64.6 German
Mention
CEAF B3
Catalan
82.7
57.1
64.6
German
59.2
49.5
50.7
English
73.9
57.3
61.3
Spanish
83.1
59.3
66.0

44. Coreference resolution
Method of (Lee, Peirsman et al. 2011), which was the best-performing in CoNLL-2011
Based on locating all noun phrases, identifying their properties, and then clustering them in several deterministic iterations (called sieves), starting with the highest-confidence rules and moving to lower-confidence higher-recall ones.
Eager approach: any matching noun phrases with matching properties are immediately clustered together.

45. Identify opinion orientation
For each feature, we identify the sentiment or opinion orientation expressed by a reviewer.
Almost all approaches make use of opinion words and phrases. But notice again (a simplistic way):
Some opinion words have context independent orientations, e.g., “great”.
Some other opinion words have context dependent orientations, e.g., “small”
Many ways to use opinion words.
Machine learning methods for sentiment classification at the sentence and clause levels are also applicable.

46. Aggregation of opinion words (Ding and Liu, 2008)
Input: a pair (f, s), where f is a product feature and s is a sentence that contains f.
Output: whether the opinion on f in s is positive, negative, or neutral.
Two steps:
Step 1: split the sentence if needed based on BUT words (but, except that, etc).
Step 2: work on the segment sf containing f. Let the set of opinion words in sf be w1, .., wn. Sum up their orientations (1, -1, 0), and assign the orientation to (f, s) accordingly.
In (Ding et al, WSDM-08), step 2 is changed to
with better results. wi.o is the opinion orientation of wi. d(wi, f) is the distance from f to wi.

47. Basic Opinion Rules (Liu, Ch. in NLP handbook)
Opinions are governed by some rules, e.g.,
Neg  Negative
Pos  Positive
Negation Neg  Positive
Negation Pos  Negative
Desired value range  Positive
Below or above the desired value range  Negative

48. Basic Opinion Rules (Liu, Ch. in NLP handbook)
Decreased Neg  Positive
Decreased Pos  Negative
Increased Neg  Negative
Increased Pos  Positive
Consume resource  Negative
Produce resource  Positive
Consume waste  Positive
Produce waste  Negative

49. 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.”
(many are much more complex).
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.”

50. Comparative Opinions (Jindal and Liu, 2006)
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”

51. Mining Comparative Opinions
Objective: Given an opinionated document d,. Extract comparative opinions:
(O1, O2, F, po, h, t),
where O1 and O2 are the object sets being compared based on their shared features F, po is the preferred object set of the opinion holder h, and t is the time when the comparative opinion is expressed.
Note: not positive or negative opinions.

52. Sentiment Lexicon

53. Sentiment (opinion) lexicon
Sentiment lexicon: lists of words and expressions used to express people’s subjective feelings and sentiments/opinions
sentiments/opinions.
Not just individual words, but also phrases and idioms, e.g.
“costs an arm and a leg”
Many sentiment lexica can be found on the web
They often have thousands of terms, and are quite useful

54. Sentiment lexicon Sentiment words or phrases (also called polar words,
opinion bearing words, etc). E.g.,
Positive: beautiful, wonderful, good, amazing,
Negative: bad, poor, terrible, cost an arm and a leg.
Many of them are context dependent, not just application domain dependent.
Three main ways to compile such lists:
Manual approach: not a bad idea for a one-time effort
Corpus-based approach
Dictionary-based approach

55. Corpus vs Dictionary-based method
Corpus-based approaches
Often use a double propagation between opinion words and the items they modify
require a large corpus to get good coverage
Dictionary-based methods
Typically use WordNet’s synsets and hierarchies to acquire opinion words
usually do not give domain or context dependent meanings

56. Corpus-based approaches
Rely on syntactic patterns in large corpora.
(Hazivassiloglou and McKeown, 1997; Turney, 2002; Yu and Hazivassiloglou, 2003; Kanayama and Nasukawa, 2006; Ding, Liu and Yu, 2008)
Can find domain dependent orientations (positive, negative, or neutral).
(Turney, 2002) and (Yu and Hazivassiloglou, 2003)
are similar.
Assign opinion orientations (polarities) to words/phrases.
(Yu and Hazivassiloglou, 2003) is slightly different from (Turney, 2002)
use more seed words (rather than two) and use log-likelihood ratio (rather than PMI).

57. The Double Propagation method
The DP method can also use dependency of opinions & aspects to extract new opinion words.
Based on dependency relations
Knowing an aspect can find the opinion word that modifies it
E.g. “The rooms are spacious”
Knowing some opinion words can find more opinion words
E.g. “The rooms are spacious and beautiful”
Jijkoun, Rijke and Weerkamp (2010) did similarly

58. Opinions implied by objective terms
Most opinion words are adjectives and adverbs, e.g., good, bad, etc
There are also many subjective and opinion verbs and nouns, e.g., hate (VB), love (VB), crap (NN).
But objective nouns can imply opinions too
E.g. “After sleeping on the mattress for one month, a body impression has formed in the middle”
How to discover such nouns in a domain or context?

59. Pruning
For an aspect with an implied opinion, it has a fixed opinion, either +ve or -ve, but not both.
We find two direct modification relations using a dependency parser.
Type 1: O → O-Dep → A
This TV has good picture quality
Type 2: O → O-Dep → H ← A-Dep ← A
E.g. The springs of the mattress are bad
If an aspect has mixed opinions based on the two dependency relations, prune it.

60. Dictionary-based methods
Typically use WordNet’s synsets and hierarchies to acquire opinion words
Start with a small seed set of opinion words.
Bootstrap the set to search for synonyms and antonyms in WordNet iteratively (Hu and Liu, 2004; Kim and Hovy, 2004; Valitutti, Strapparava and Stock, 2004; Mohammad, Dunne and Dorr, 2009).
Kamps et al., (2004) proposed a WordNet distance method to determine the sentiment orientation of a given adjective

61. Semi-supervised learning (Esuli and Sebastiani, 2005)
Use supervised learning
Given two seed sets: positive set P, negative set N
The two seed sets are then expanded using synonym and antonymy relations in an online dictionary to generate the expanded sets P’ and N’
P’ and N’ form the training sets
Using all the glosses in a dictionary for each term in P’  N’ and converting them to a vector
Build a binary classifier
SentiWordnet

62. Which approach to use?
Both corpus and dictionary based approaches are needed.
Dictionary usually does not give domain or context dependent meanings
Corpus is needed for that
Corpus-based approach is hard to find a very large set of opinion words
Dictionary is good for that
In practice, corpus, dictionary and manual approaches are all needed.

63. Spam Detection

64. Opinion Spam Detection (Jindal and Liu, 2007, 2008)
Fake/untruthful reviews:
Write undeserving positive reviews for some target objects in order to promote them.
Write unfair or malicious negative reviews for some target objects to damage their reputations.
Increasing number of customers wary of fake reviews (biased reviews, paid reviews)

65. An Example Practice of Review Spam
Belkin International, Inc
Top networking and peripherals manufacturer | Sales ~ $500 million in 2008
Posted an ad for writing fake reviews on amazon.com (65 cents per review)
Jan 2009

66. Experiments with Amazon Reviews
June 2006
5.8mil reviews, 1.2mil products and 2.1mil reviewers.
A review has 8 parts
<Product ID> <Reviewer ID> <Rating> <Date> <Review Title> <Review Body> <Number of Helpful feedbacks> <Number of Feedbacks> <Number of Helpful Feedbacks>
Industry manufactured products “mProducts”
e.g. electronics, computers, accessories, etc
228K reviews, 36K products and 165K reviewers.

67. Some Tentative Results
Negative outlier reviews tend to be heavily spammed
Those reviews that are the only reviews of some products are likely to be spammed
Top-ranked reviewers are more likely to be spammers
Spam reviews can get good helpful feedbacks and non-spam reviews can get bad feedbacks

68. Meeting Social Sciences
Extract and analyze political opinions.
Candidates and issues
Compare opinions across cultures and lang.
Comparing opinions of people from different countries on the same issue or topic, e.g., Internet diplomacy
Opinion spam (fake opinions)
What are social, culture, economic aspects of it?
Opinion propagation in social contexts
How opinions on the Web influence the real world
Are they correlated?
Emotion analysis in social context & virtual world

69. WebSays + Tiscali
17/1/2013

70. Summary We briefly defined sentiment analysis problem.
Direct opinions: focused on feature level analysis
Comparative opinions: different types of comparisons
Opinion spam detection: fake reviews.
Currently working with Google (Google research award).
A lot of applications.
Technical challenges are still huge.
But I am quite optimistic.
Interested in collaboration with social scientists
opinions and related issues are inherently social.

71. References
B. Liu, “Sentiment Analysis and Subjectivity.” A Chapter in Handbook of Natural Language Processing, 2nd Edition, 2010.