1. Prof. Leticia Arco García leticiaarco@gmail.com
Central University of Las Villas, Cuba
Artificial intelligence Lab
Computer Science Department
Opinion Mining
Prof. Leticia Arco García

2. Motivation Someone who wants to buy a car
Looks for comments and reviews
Someone who just bought a car
Comments on it
Writes about his experience
Car manufacturer
Gets feedback from customers
Improve their products
Adjust marketing strategies

3. This is not only true for individuals but also true for organizations
Opinions
Opinions are central to almost all human activities and are key influencers of our behaviours
Our beliefs and perceptions of reality, and the choices we make, are, to a considerable degree, conditioned upon how others see and evaluate the world
When we need to make a decision
we often seek out the opinions of others
This is not only true for individuals
but also true for organizations

4. Social media on the Web
With the explosive growth of social media on the Web, individuals and organizations are increasingly using the content in these media for decision making

5. Contents Origin and definition Different levels of analysis
Opinions: definition, types and main problems
Sentiment analysis tasks
Polarity detection: two approaches
Other sentiment analysis proposals
Lexical resources and available datasets
Applications in Business Informatics
Our results
Challenges: SemEval-2017 and TASS 2017

6. Origin (1/2)
Some earlier work on interpretation of metaphors, sentiment adjectives, subjectivity, view points and affects ( )
Learning subjective adjectives from corpora (Wiebe, 2000)
Yahoo! for Amazon: Extracting market sentiment from stock message boards (Dans and Chen, 2001)
An operational system for detecting and tracking opinions in on-line discussion (Tong, 2001)

7. Origin (2/2) Sentiment analysis
Sentiment analysis: Capturing favourability using natural language processing (Nasukawa and Yi, 2003)
Opinion mining
Mining the peanut gallery: Opinion extraction and semantic classification of product reviews (Dave et. al., 2003)

8. Subjectivity analysis
Definition
Sentiment analysis is the field of study that analyses
people’s opinions
sentiments
evaluations
appraisals
attitudes
emotions
towards entities such as
products
services
organizations
individuals
issues
events
topics
their attributes
Emotion analysis
Review mining
Opinion extraction
Sentiment analysis
Subjectivity analysis
Opinion mining
Sentiment mining
Affect analysis

9. Different levels of analysis (1/2)
Document level
Classifies whether a whole opinion document expresses a positive or negative sentiment
Assumption: Each document expresses opinions on a single entity
Sentence level
Determines whether each sentence expresses a positive, negative or neutral opinion
Closely related to subjectivity classification
“The iPhone’s call quality is good, but its battery life is short.”
Both levels do not discover what exactly people liked and did not like

10. Different levels of analysis (2/2)
Entity and aspect level
Performs finer-grained analysis
Directly looks at the opinion itself
Goal: discover sentiments on entities and/or their aspects
“The iPhone’s call quality is good, but its battery life is short.”
Entity: iPhone
Aspects: call quality and battery life
Sentiment on iPhone’s call quality: positive
Sentiment on its battery life: negative

11. Opinion The meaning of opinion itself is still very broad
Sentiment analysis mainly focuses on opinions which express or imply positive or negative sentiments
1, 2, 3 and 4 positive
5 negative
Opinion source
Opinion holders
Date
Sentiment orientations
Opinion polarities
Topic

12. Opinion definition
An opinion is a quintuple (ei, aij, sijkl, hk, tl) where:
ei is the name of an entity
aij is an aspect of ei
sijkl is the sentiment on aspect aij of entity ei
hk is the opinion holder
tl is the time when the opinion is expressed by hk
The sentiment sijkl is positive, negative, or neutral, or expressed with different strength/intensity level, e.g., 1 to 5

13. Types of opinions (1/2) Regular opinions
Express a sentiment only on a particular entity or an aspect of the entity
Direct opinion
“Belgian chocolates taste very good.”
Indirect opinion
“After injection of the drug, my joints felt worse.”
Comparative opinions
Compare multiple entities based on some of their shared aspects
“Belgian beers taste much better than Cuban beers.”

14. Types of opinions (2/2)
Explicit opinion
Is a subjective statement that gives a regular or comparative opinion
“UHASSELT is a very good university.”
Implicit opinion
Is an objective statement that implies a regular or comparative opinion
“The battery life of Nokia phones is longer than Samsung phones.”
Explicit opinions are easier to detect and to classify than implicit opinions

15. Sentiment analysis is a NLP problem
It touches every aspect of NLP
Co-reference resolution
Negation handling
Word sense disambiguation
Sentiment analysis is a highly restricted NLP problem
It does not need to fully understand the semantics of each sentence or document
It only needs to understand some aspects of it
Positive or negative sentiments
Their target entities
Their topics

16. Opinion Mining is more difficult than Text Mining
Informal language
Abbreviations
Emoticons
Spelling and typographical errors
Ironic and sarcastic language
Language knowledge level
Cultural level
These characteristics impose a greater difficulty on the opinion mining, regarding other text mining tasks

17. Sentiment analysis tasks
Objective of sentiment analysis: Given an opinion document, discover all opinion quintuples
Entity extraction and categorization
Aspect extraction and categorization
Opinion holder extraction and categorization
Time extraction and standardization
Aspect sentiment classification
Opinion quintuple generation

18. Polarity detection: two approaches
Semantic approaches
Characterized by the use of dictionaries of words (lexicons) with semantic orientation of polarity or opinion
Computational learning techniques
Consist on training a classifier using any supervised learning algorithm from a collection of annotated texts

19. Words expressing feeling or opinion
Positive opinion: good, wonderful, amazing, …
Negative opinion: bad, poor, terrible, …
Sentiment lexicon or opinion lexicon
(sentiment words, opinion words, polar words, opinion-bearing words)
Base type
Comparative type

20. Approaches to compile sentiment words
Manual approach
Labour intensive and time consuming
Useful for final check in automated approaches
Dictionary-based approach
Few seed sentiment words to bootstrap based on the synonym and antonym structure of a dictionary
Corpus-based approach
Given a seed list of known sentiment words, discover other sentiment words and their orientations from a domain corpus
Adapt a general-purpose sentiment lexicon to a new one using a domain corpus for sentiment analysis applications in the domain

21. Sentiment lexicon
Although sentiment words and phrases are important for sentiment analysis, only using them is far from sufficient
Sentiment lexicon is necessary but not sufficient for sentiment analysis

22. Some problems of feeling words
They may have opposite orientations in different application domains
A sentence containing sentiment words may not express any sentiment
Sarcastic sentences with or without sentiment words are hard to deal with
Many sentences without feeling words can also imply opinions
“It is a large dictionary, covering thousands of words.”
“He has put on weight, and is now quite large.”
“He likes to talk large, but I think he exaggerates.”
“Can you tell me which camera is good?”
“If I can find a good camera in the shop, I'll buy it.”
“He killed the ant before it could bite him.”
“I HATE to admit it but, I LOVE admitting things.”
“I liiikeee winter, summer does not arrive yet :-(“
“What a great car! It stopped working in two days.”
“This washer uses a lot of water.”

23. Sentiment classification using supervised learning (1/3)
Two-class classification problem: positive and negative
Training and testing data used are normally product reviews
A review with 4 or 5 stars is considered a positive review
A review with 1 to 2 stars is considered a negative review
First approaches:
Naïve Bayes classification
Support Vector Machines

24. Sentiment classification using supervised learning (2/3)
Like other supervised machine learning applications, the key for sentiment classification is the engineering of a set of effective features
Terms and their frequency
Part of speech
Sentiment words and phrases
Rules of opinions
Sentiment shifters
Syntactic dependency

25. Sentiment classification using supervised learning (3/3)
Apart from classification of positive and negative sentiments, researchers also studied the problem of predicting the rating scores (e.g., 1–5 stars) of reviews
Regression problem

26. Subjectivity classification
Objective sentences
Express factual information from sentences
Subjective sentences
Express subjective views and opinions
Is subjectivity equivalent to sentiment?
“I think that he went home.”
“The phone broke in two days.”

27. Emotion Emotions are our subjective feelings and thoughts
Six primary emotions: love, joy, surprise, anger, sadness and fear
Opinions that we study in sentiment analysis are mostly evaluations
Rational evaluations are from rational reasoning, tangible beliefs, and utilitarian attitudes
Emotional evaluations are from non-tangible and emotional responses to entities which go deep into people’s state of mind
Five sentiment ratings
emotional negative (-2), rational negative (-1), neutral (0), rational positive (+1), and emotional positive (+2)

28. Aspect-based sentiment analysis
Such methods are typically unsupervised
Sentiment lexicon
Composite expressions
Rules of opinions
Sentence parse tree
Sentiment shifters
But-clauses
Aggregate opinions

29. Aspect extraction approaches
Extraction based on frequent nouns and noun phrases
Extraction by exploiting opinion and target relations
Extraction using supervised learning
Extraction using topic modelling
Semantic classification and deep learning

30. Grouping aspects into categories
Aspect expressions need to be grouped into synonymous aspect categories
Each category represents a unique aspect
Same aspect for phones: “call quality” and “voice quality”
Grouping such aspect expressions from the same aspect is critical for opinion analysis
WordNet and other thesaurus
“movie” and “picture” are synonyms in movie reviews
“picture” is more likely to be synonymous to “photo” while “movie” to “video” in camera reviews
Many aspect expressions are multi-word phrases, which cannot be easily handled with dictionaries

31. Opinion summarization (1/3)
Different entity names
Aspect-based opinion summary
Different aspect names

32. Opinion summarization (2/3)
Visualization of aspect-based summary of opinions on a digital camera

33. Opinion summarization (3/3)
Visualization of aspect-based summaries of opinions

34. Businesses that provide fake review writing services
Opinion spammers
A key feature of social media is that it enables anyone from anywhere in the
world to freely express his/her views and opinions without disclosing his/her true identify and without the fear of undesirable consequences
Opinion spammers
Friends and family
Competitors
Company employees
Businesses that provide fake review writing services
Genuine customers
Some businesses give discounts and even full refunds to some of their customers on the condition that the customers write positive reviews for them
Agencies and political organizations may employ people to post messages to secretly influence social media conversations and to spread lies and disinformation

35. Opinion spammers vs opinion spam detection
Review content: linguistic features
Meta-data about the review: user-id, star rating, time, host IP address, …
Product information
Opinion spam detection
Supervised
Unsupervised
There is no labelled training data for learning
Exploit duplicate reviews
Create features
Spam detection based on atypical behaviours
Spam detection using review graph
Group spam detection:
Frequent pattern mining

36. Cross-domain sentiment classification
A classifier trained using opinion documents from one domain often performs poorly on test data from another domain
Words and even language constructs used in different domains for expressing opinions can be quite different
The same word in one domain may mean positive but in another domain may mean negative
Learn as humans do
Retain learned knowledge from previous tasks and use it to help future learning
Is a continuous learning process where the learner has performed a sequence of learning tasks
Domain adaptation or transfer learning is needed
A small amount of labelled training data for the new domain
No labelled data for the new domain
Lifelong machine learning

37. Cross-language sentiment classification
Perform sentiment classification of opinion documents in multiple languages
Researchers from different countries want to build sentiment analysis systems in their own languages.
Companies want to know and compare consumer opinions about their products and services in different countries
Co-training methods
Lexical resources

38. Opinion search and retrieval
Find public opinions about a particular entity or an aspect of the entity
Find customer opinions about a digital camera
Find opinions of a person or organization (i.e., opinion holder) about a particular entity or an aspect of the entity (or topic)
Find Charles Michel’s opinion about terrorism

39. Lexical resources
WordNet Affect
SentiWordNet
General Inquirer

40. WordNet Affect
WordNet-Affect is an extension of WordNet Domains, including a subset of synsets suitable to represent affective concepts correlated with affective words
Affective labels (a-labes) are assigned to a number of WordNet synsets

41. WordNet Affect: Terms and affective categories

42. Some terms related to "university" through their emotional categories

43. SentiWordNet SentiWordNet is a lexical resource for opinion mining
SentiWordNet assigns to each synset of WordNet three sentiment scores:
Positivity
Negativity
Objectivity
Generating SentiWordNet
1. A weak-supervision, semi-supervised learning step
2. A random-walk step

44. General Inquirer Harvard categories:
Positive, Negative, Strong, Week, Active, Passive, …
Pleasure, Pain, Feel, Arousal, Virtue, Emotion, …
New categories based social cognition
Lasswell value dictionary categories

45. Some public available datasets
Stanford large movie dataset
Movie
TripAdvisor
TBOD
ISEAR
DUC data
Spinn3r dataset
HASH
EMOT
OpinRank dataset

46. Opinion mining and enterprises
Enterprises are open and flexible in the use of technological tools to “sense” customers and market
Acquiring information in real-time allows the company to be agile and to develop ”Sense and Response” capabilities
An agile enterprise respond immediately to any internal or external event as customer demand or customer opinions
Knowing what the customer thinks of a given product/service helps top management to introduce improvements in processes and products
Customer opinions represent a potential of knowledge to be consider for the acquisition of competitive advantages

47. Opinions are very important for decision making
Gretzel and Yoo (2008) demonstrate that 97.7% of travel booking decisions are made after consulting other travellers’ opinions, of which 77.9% involve the use of customer reviews as a source of information helping to make a better decision
Gretzel, U. & Yoo, K. H. (2008) Use and Impact of Online Travel Reviews Information and Communication Technologies in Tourism. Innsbruck, Austria.

48. How can a sentiment analysis tool help my brand?
Better understand the motivations behind sentiment
Learn from social posts, news, reviews, and more
Benchmark against competitors
Track purchase intent
Evaluate campaign impact
Analyse product launch response

49. Some sentiment analysis tools
Opinion Crawl
Meaning cloud
Trackur
SAS
Opentext
Statsoft
NetOwl Extractor
Meltwater

50. Cloud‐based Event‐processing Architecture for Opinion Mining (1/2)
Smart distributed architecture for opinion mining on internet-based content that answers key challenges:
Integrating heterogeneous data sources
Adapting to events through dynamic system configuration
A novel approach of semantic complex event processing in a cloud environment capturing different levels of information:
Event data
Content from various heterogeneous sources
Distributed sources
Dynamic co-reference resolution

51. Cloud‐based Event‐processing Architecture for Opinion Mining (2/2)
Topic modelling and sentiment analysis
Deep linguistic and interlinking analysis
Transfer learning and active learning of opinions
Cloud computing and event processing

52. Enterprise information fusion for real-time business intelligence (1/2)
Correlate the external events in real-time with known facts about the internal operations and transactions of the enterprise and its ecosystem

53. News event detection from Twitter
Enterprise information fusion for real-time business intelligence (2/2)
News event detection from Twitter

54. Identifying customer preferences about tourism products using an aspect-based opinion mining approach

55. A novel application mining for competitive intelligence
A new method to extract opinion patterns from customer reviews and its application to evaluate resources or internal factors in an enterprise
Opinion gathering
Text pre-processing
Factor and polarity detection
Internal factor evaluation

56. Customer voice sensor
Call centre is an important intermediary between enterprise and customers
It helps customers to solve the problems
It allows the enterprise to deeply analyse the customer's voice and make a distinct market positioning
A comprehensive opinion mining system for call centre conversation

57. Mobile application for customers’ reviews opinion mining

58. The Power of Text-mining in Business Process Management (1/2)

59. The Power of Text-mining in Business Process Management (2/2)

60. PosNeg Opinion Opinions Identify terms
Disambiguate lexically each term
Obtain all meanings of each term
Classify each term in positive or negative
Evaluate the opinion

61. Improving SentiWordNet 3.0
84342 terms
Preprocessing stage: Split terms considering if they have polarity values assigned or not
5037
79305 terms
Stage 1: Assign polarity values considering the synonyms of terms without assigned polarity values
51027
28278 terms
Stage 2: Assign inverse polarity values considering the antonyms of terms without assigned polarity values
5678
22600 terms
Stage 3: Assign polarity values considering the synonyms of terms with assigned polarity values
5770
16830 terms
Stage 4: Assign inverse polarity values considering the antonyms of terms with assigned polarity values
1539
15291 terms without assigned polarity values
69051 terms with assigned polarity values

62. SpanishSentiWordNet Intralinguistic index
agresor n attacker assailant aggressor assaulter aggressor robber
Intralinguistic index
The spanish term and its POS label
English meaning of the term
Improved SentiWordNet 3.0
Negative and positive polarities of each meaning
Evaluate the polarity of the term by adding the positive and negative polarities of its meanings
Negative and positive polarities of the Spanish term
SpanishSentiWordNet

63. Topic detection assisting polarity detection
We got a large room with 2 double beds and 2 bathrooms, The TV was Ok, a 27' CRT Flat Screen.
We stay at Hilton for 4 nights last march. It was a pleasant stay. We got a large room with 2 double beds and 2 bathrooms, The TV was Ok, a 27' CRT Flat Screen. The concierge was very friendly when we need. The room was very cleaned when we arrived, we ordered some pizzas from room service and the pizza was Ok also. The main Hall is beautiful. The breakfast is charged, 20 dollars, kinda expensive. The internet access (WiFi) is charged, 13 dollars/day. Pros: Low rate price, huge rooms, close to attractions at Loop, close to metro station. Cons: Expensive breakfast, Internet access charged. Tip: When leaving the building, always use the Michigan Av exit. Its a great view.
The concierge was very friendly when we need.
The breakfast is charged, 20 dollars, kinda expensive.
The room was very cleaned when we arrived

64. Schema for topic segmentation and detection
Textual corpora
Identify textual units
textual units
Pre-process
tokens
Represent textual units
vectors, graphs, probabilistic distribution
Segment
segments
Represent segments
Cluster segments
segment clusters (topics)
Label segment clusters
Topics and corresponding labels
Framework OpinionTopicDetection
Desktop application OpinionTD

65. Open issues and future directions (1/2)
Data collected from various resources are often so much noisy, wrongly spelt and unstructured
There is a lack of universal opinion grading system across sentiment dictionaries
Online discussion and political discussions often contain irony and sarcastic sentences
For better product comparison, we should compare a set of products with respect to their common aspects
The lack of proper review spam dataset is a major issue in order to perform opinion spam detection

66. Open issues and future directions (2/2)
A very few attempts were made to utilize the potential of optimization techniques for feature selection
There is a lack of opinion mining system in non-English languages
Cross-domain sentiment analysis is still a major challenge
Aspect level sentiment analysis is very much required for comparative visualization of similar kind of products
The main challenge lies in review helpfulness is the validation of the proposed method

67. Challenges SemEval 2017 Detecting sentiment, humor, and truth
Task 4: Sentiment Analysis in Twitter
Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Task 6: #HashtagWars: Learning a Sense of Humor
Task 7: Detection and Interpretation of English Puns
Task 8: RumourEval: Determining rumour veracity and support for rumours
TASS 2017
Task 1: Sentiment analysis at tweet level
Task 2: Aspect-based sentiment analysis

68. Prof. Leticia Arco García leticiaarco@gmail.com
Central University of Las Villas, Cuba
Artificial intelligence Lab
Computer Science Department
Thanks!
Questions, ideas, suggestions, comments, …
Opinion Mining
Prof. Leticia Arco García