Sentiment Analysis
michel.bruley@teradata.com
Extract from various presentations: Bing Liu, Aditya Joshi, Aster Data …
www.decideo.fr/bruley
January 2012
Introduction

Two main types of textual information: Facts and Opinions

Most current text information processing methods work
with factual information (e.g., web search, text mining)

Sentiment analysis or opinion mining, computational study
of opinions (sentiments, emotions) expressed in text

Why opinion mining now? Mainly because of the Web huge
volumes of opinionated text.
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What is Sentiment Analysis?

Identify the orientation of opinion in a piece of text (blogs,
user comments, review websites, community websites, …), in
others words determine if a sentence or a document
expresses positive, negative, neutral sentiment towards some
object?
The movie
was fabulous!
[ Sentimental ]
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The movie
stars Mr. X
[ Factual ]
The movie
was horrible!
[ Sentimental ]
SA at different levels
His last movie was
Theand
movie
was
Great
interesting.
The
His
The
last
police
movie
movie
stopped
was
was
interesting and
very
corruption
great.
boring
Thisfabulous
one’s a dud.
Word-level SA
Sentence-level SA
Document-level SA
fabulous
interesting
boring
police (subj.) stopped (verb) corruption (obj.)
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What is an Opinion?

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
– hi is an opinion holder
– tl is the time when the opinion is expressed
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Objective: structure the unstructured
 Objective:
Given an opinionated document,
– Discover all quintuples (oj, fjk, soijkl, hi, tl),
• i.e., mine the five corresponding pieces of information
in each quintuple
 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

With all quintuples, all kinds of analyses become possible
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SA is not Just ONE Problem

Track direct opinions:
– document
– sentence
– feature level

Compare opinions: different types of comparisons

Detect opinion spam detection: fake reviews
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Polarity Classifier

First eliminate objective sentences, then use remaining
sentences to classify document polarity (reduce noise)
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Level of Analysis
We can inquire about sentiment at various linguistic levels:

Words – objective, positive, negative, neutral

Clauses – “going out of my mind”

Sentences – possibly multiple sentiments

Documents
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Words



Adjectives
– objective: red, metallic
– positive: honest, important, mature, large, patient
– negative: harmful, hypocritical, inefficient
– subjective (but not positive or negative): curious, peculiar, odd,
likely, probable
Verbs
– positive: praise, love
– negative: blame, criticize
– subjective: predict
Nouns
– positive: pleasure, enjoyment
– negative: pain, criticism
– subjective: prediction, feeling
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Clauses

Might flip word sentiment
– “not good at all”
– “not all good”

Might express sentiment not in any word
– “convinced my watch had stopped”
– “got up and walked out”
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Some Problems
 Which
features to use? Words (unigrams), Phrases/n-grams,
Sentences
 How to interpret features for sentiment detection? Bag of
words (IR), Annotated lexicons (WordNet, SentiWordNet),
Syntactic patterns, Paragraph structure
 Must consider other features due to…
– Subtlety of sentiment expression
• irony
• expression of sentiment using neutral words
– Domain/context dependence
• words/phrases can mean different things in different
contexts and domains
– Effect of syntax on semantics
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Some Applications Examples

Review classification: Is a review positive or negative
toward the movie?

Product review mining: What features of the ThinkPad
T43 do customers like/dislike?

Tracking sentiments toward topics over time: Is anger
ratcheting up or cooling down?

Prediction (election outcomes, market trends): Will
Obama or Republican candidate win?

Etcetera
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Aster Data position for Text Analysis
Data
Acquisition
Gather text from relevant
sources
(web crawling, document
scanning, news feeds,
Twitter feeds, …)
Pre-Processing
Mining
Analytic
Applications
Perform processing
required to transform and
store text data and
information
Apply data mining
techniques to derive
insights about stored
information
Leverage insights from
text mining to provide
information that improves
decisions and processes
(stemming, parsing, indexing,
entity extraction, …)
(statistical analysis,
classification, natural
language processing, …)
(sentiment analysis, document
management, fraud analysis,
e-discovery, ...)
Aster Data Fit
Third-Party Tools Fit
Aster Data Value: Massive scalability of text storage and processing, Functions for text processing, Flexibility to develop diverse
custom analytics and incorporate third-party libraries
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