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Approaches to Sentiment Analysis. MSE 2400 EaLiCaRA Spring 2014 Dr. Tom Way. Based in part on notes from Aditya Joshi. What is Sentiment Analysis?. Identify the orientation of opinion in a piece of text Can be generalized to a wider set of emotions. The movie was fabulous!.

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Approaches to sentiment analysis

Approaches toSentiment Analysis

MSE 2400 EaLiCaRA

Spring 2014 Dr. Tom Way

Based in part on notes from Aditya Joshi


What is sentiment analysis
What is Sentiment Analysis?

  • Identify the orientation of opinion in a piece of text

  • Can be generalized to a wider set of emotions

The movie

was fabulous!

The movie

stars Mr. X

The movie

was horrible!

MSE 2400 Evolution & Learning


Motivation
Motivation

  • Knowing sentiment is a very natural ability of a human being.

    Can a machine be trained to do it?

  • SA aims at getting sentiment-related knowledge especially from the huge amount of information on the internet

  • Can be generally used to understand opinion in a set of documents

MSE 2400 Evolution & Learning


Tripod of sentiment analysis
Tripod of Sentiment Analysis

Cognitive Science

Sentiment Analysis

Machine Learning

Machine Learning

Natural Language Processing

Natural Language Processing

MSE 2400 Evolution & Learning


Challenges
Challenges

  • Contrasts with standard text-based categorization

  • Domain dependent

  • Sarcasm

  • Thwarted expressions

  • Contrasts with standard text-based categorization

  • Domain dependent

  • Sarcasm

  • Thwarted expressions

  • Contrasts with standard text-based categorization

  • Domain dependent

  • Sarcasm

  • Thwarted expressions

  • Contrasts with standard text-based categorization

  • Domain dependent

  • Sarcasm

  • Thwarted expressions

Mere presence of words is

Indicative of the category

in case of text categorization.

Not the case with

sentiment analysis

Sentiment of a word

is w.r.t. the

domain.

Example: ‘unpredictable’

For steering of a car,

For movie review,

Sarcasm uses words of

a polarity to represent

another polarity.

Example: The perfume is so

amazing that I suggest you wear it

with your windows shut

the sentences/words that

contradict the overall sentiment

of the set are in majority

Example: The actors are good,

the music is brilliant and appealing.

Yet, the movie fails to strike a chord.

MSE 2400 Evolution & Learning


Quantifying sentiment
Quantifying sentiment

Positive

Negative

Subjective Polarity

Term sense position

Objective Polarity

Each term has a Positive, Negative and Objective score. The scores sum to one.

MSE 2400 Evolution & Learning


Approach 1 using adjectives
Approach 1: Using adjectives

  • Many adjectives have high sentiment value

    • A ‘beautiful’ bag

    • A ‘wooden’ bench

    • An ‘embarrassing’ performance

  • Focusing on adjectives might be beneficial

MSE 2400 Evolution & Learning


Approach 2 using adverb adjective combinations aacs
Approach 2: Using Adverb-Adjective Combinations (AACs)

  • Calculate sentiment value based on the effect of adverbs on adjectives

  • Linguistic ideas:

  • Adverbs of affirmation: certainly

  • Adverbs of doubt: possibly

  • Strong intensifying adverbs: extremely

  • Weak intensifying adverbs: scarcely

  • Negation and Minimizers: never

MSE 2400 Evolution & Learning


Approach 3 subject based sa
Approach 3: Subject-based SA

  • Examples:

The horse bolted.

The movie lacks a good story.

MSE 2400 Evolution & Learning


Lexical analysis
Lexical Analysis

subj. bolt

Argument that receives the sentiment (subj./obj.)

b VB bolt subj

subj. lack obj.

b VB lack obj ~subj

Argument that receives the sentiment (subj./obj.)

Argument that sends the sentiment (subj./obj.)

MSE 2400 Evolution & Learning


Example
Example

The movie lacks a good story.

The movie lacks \S+.

Lexicon :

Steps :

  • Consider a context window of upto five words

  • Shallow parse the sentence

  • Step-by-step calculate the sentiment value based on lexicon and by adding ‘\S+’ characters at each step

G JJ good obj.

B VB lack obj ~subj.

MSE 2400 Evolution & Learning


Applications
Applications

  • Review-related analysis

  • Developing ‘hate mail filters’ analogous to ‘spam mail filters’

  • Question-answering (Opinion-oriented questions may involve different treatment)

MSE 2400 Evolution & Learning


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