• Sentiment =

  • Feelings, Attitudes, Emotions, Opinions

  • A thought, view, or attitude, especially one based mainly on emotion instead of reason

• Subjective impressions, not facts

• Emotion: brief organically synchronized … evaluation of a major event
  • angry, sad, joyful, fearful, ashamed, proud, elated
• Mood: diffuse non-caused low-intensity long-duration change in subjective feeling
  • cheerful, gloomy, irritable, listless, depressed, buoyant
• Interpersonal stances: affective stance toward another person in a specific interaction
  • friendly, flirtatious, distant, cold, warm, supportive, contemptuous
• Attitudes: enduring, affectively colored beliefs, dispositions towards objects or persons
  • liking, loving, hating, valuing, desiring
• Personality traits: stable personality dispositions and typical behavior tendencies
  • nervous, anxious, reckless, morose, hostile, jealous

• Use of natural language processing (NLP) and computational techniques to automate the extraction or classification of sentiment from unstructured text

• Other terms

  • Opinion mining
  • Sentiment mining
  • Subjectivity analysis

• Book: is this review positive or negative?

• Humanities:sentiment analysis for German historic plays.

• Products: what do people think about the new iPhone?

• Blog: how are people thinking about immigrants?

• Politics: who is going to win the election?

• Twitter: what is the trend today?

• Movie: is this review positive or negative (IMDB, Netflix)?

• Marketing: how is consumer confidence? Consumer attitudes?

• Healthcare: are patients happy with the hospital environment?

• 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: Comparison of more than one entity.

  • E.g., “iPhone is better than Blackberry.”

• An opinion is a quintuple
  
  ( entity, aspect, sentiment, holder, time)
  
  where

  • entity: target entity (or object).

  • Aspect: aspect (or feature) of the entity.

  • Sentiment: +, -, or neu, a rating, or an emotion.

  • holder: opinion holder.

  • time: time when the opinion was expressed.

• Simplest task:

  • Is the attitude of this text positive or negative?

• More complex:

  • Rank the attitude of this text from 1 to 5

• Advanced:

  • Detect the target, source, or complex opinion types
  • Implicit opinions or aspects

• Which features to use?

  • Words (unigrams)
  • Phrases/n-grams
  • Sentences

• How to interpret features for sentiment detection?

  • Bag-of-words
  • Annotated lexicons (WordNet, SentiWordNet)
  • Syntactic patterns
  • Paragraph structure
  • Word embedding

• Harder than topical classification, with which bag of words features perform well

• 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

• Lexicon-based (dictionary-based) methods

  • Using sentiment words and phrases: good, wonderful, awesome, troublesome, cost an arm and leg

  • Not completely unsupervised!

• Supervised learning methods: to classify reviews into positive and negative.
  • Naïve Bayes
  • Maximum Entropy
  • Support Vector Machine
  • Deep learning

• Home page: http://liwc.wpengine.com/

• 2300 words, >70 classes

• Affective Processes

  • negative emotion (bad, weird, hate, problem, tough)
  • positive emotion (love, nice, sweet)

• Cognitive Processes

  • Tentative (maybe, perhaps, guess), Inhibition (block, constraint)

• Pronouns, Negation (no, never), Quantifiers (few, many)

• Bing Liu’s Page on Opinion Mining

• http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar

• 6786 words

  • 2006 positive
  • 4783 negative

• https://github.com/aesuli/SentiWordNet

• All WordNet synsets automatically annotated for degrees of positivity, negativity, and neutrality/objectiveness

• [estimable(J,3)] “may be computed or estimated”
  \[\operatorname{Pos\ \ 0\ \ \ Neg\ \ 0\ \ \ Obj\ \ 1} \]

• [estimable(J,1)] “deserving of respect or high regard” \[\operatorname{Pos\ \ .75\ \ \ Neg\ \ 0\ \ \ Obj\ \ .25} \]

1. Extract a phrasal lexicon from reviews
2. Learn polarity of each phrase
3. Rate a review by the average polarity of its phrases

https://arxiv.org/abs/cs/0212032

• Positive phrases co-occur more with “excellent”

• Negative phrases co-occur more with “poor”

• But how to measure co-occurrence?

• PMI between two words:
  • How much more do two words co-occur than if they were independent?

\[PMI(word_1,woprd_2)=log_2{\frac{P(word_1,word_2)}{P(word_1)P(word_2)}}\]

• P(word) estimated by      hits(word)/N
• P(word1,word2) by     hits(word1 NEAR word2)/N^2

\[PMI(word_1,woprd_2)=log_2{\frac{hits(word_1 \: \mathrm{NEAR} \: word_2)}{hits(word_1)hits(word_2)}}\]

\[ \begin{align} \mathrm{Polarity}(phrase) = \mathrm{PMI}(pharse, \mathrm{''excellent''}) - \mathrm{PMI}(pharse, \mathrm{''poor''}) \\ \end{align} \]

• 410 reviews from Epinions
  • 170 (41%) negative
  • 240 (59%) positive
• Majority class baseline: 59%
• Turney algorithm: 74%
  
  
• Phrases rather than words
• Learns domain-specific information

• WordNet: online thesaurus
• Create positive (“good”) and negative seed-words (“terrible”)
• Find Synonyms and Antonyms
  • Positive Set: Add synonyms of positive words (“well”) and antonyms of negative words
  • Negative Set: Add synonyms of negative words (“awful”) and antonyms of positive words (”evil”)

Many packages and lexicons:

library(wordnet)
# download the wordnet dictionary from 
# https://wordnet.princeton.edu/download/current-version
setDict("C:/Program Files (x86)/WordNet/2.1/dict")
Sys.setenv(WNHOME = "C:/Program Files (x86)/WordNet/2.1")
synonyms("fill","VERB")

##  [1] "fill"      "fill up"   "fulfil"    "fulfill"   "make full" "meet"     
##  [7] "occupy"    "replete"   "sate"      "satiate"   "satisfy"   "take"

library(tidytext)
afinn_sentiments <- get_sentiments("afinn")
afinn_sentiments

## # A tibble: 2,477 x 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # i 2,467 more rows

nrc_sentiments <- get_sentiments("nrc")
nrc_sentiments

## # A tibble: 13,901 x 2
##    word        sentiment
##    <chr>       <chr>    
##  1 abacus      trust    
##  2 abandon     fear     
##  3 abandon     negative 
##  4 abandon     sadness  
##  5 abandoned   anger    
##  6 abandoned   fear     
##  7 abandoned   negative 
##  8 abandoned   sadness  
##  9 abandonment anger    
## 10 abandonment fear     
## # i 13,891 more rows

loughran_sentiments <- get_sentiments("loughran")
loughran_sentiments

## # A tibble: 4,150 x 2
##    word         sentiment
##    <chr>        <chr>    
##  1 abandon      negative 
##  2 abandoned    negative 
##  3 abandoning   negative 
##  4 abandonment  negative 
##  5 abandonments negative 
##  6 abandons     negative 
##  7 abdicated    negative 
##  8 abdicates    negative 
##  9 abdicating   negative 
## 10 abdication   negative 
## # i 4,140 more rows

bing_sentiments <- get_sentiments("bing")
bing_sentiments

## # A tibble: 6,786 x 2
##    word        sentiment
##    <chr>       <chr>    
##  1 2-faces     negative 
##  2 abnormal    negative 
##  3 abolish     negative 
##  4 abominable  negative 
##  5 abominably  negative 
##  6 abominate   negative 
##  7 abomination negative 
##  8 abort       negative 
##  9 aborted     negative 
## 10 aborts      negative 
## # i 6,776 more rows

• Advantages:
  • Can be domain-specific
  • Can be more robust (more words)
• Intuition
  • Start with a seed set of words (‘good’, ‘poor’)
  • Find other words that have similar polarity:
    • Using “and” and “but”
    • Using words that occur nearby in the same document
    • Using WordNet synonyms and antonyms

• Classify a document (e.g., a review) based on the overall sentiment of the opinion holder
  • Classes: Positive, negative (possibly neutral)
• An example review:
  • “I bought an iPhone a few days ago. It is such a nice phone, although a little large. The touch screen is cool. The voice quality is great too. I simply love it!”
  • Classification: positive or negative?
• It is basically a text classification problem

• Classify the sentiment expressed in a sentence
  • Classes: positive, negative, neutral
  • Neutral means no sentiment expressed
    • “I believe he went home yesterday.”
    • “I bought a iPhone yesterday”
• But bear in mind
  • Explicit opinion: “I like this car.”
  • Fact-implied opinion: “I bought this car yesterday and it broke today.”
  • Mixed opinion: “Apple is doing well in this poor economy”

• The problem has been studied by numerous researchers.

• Key: feature engineering. A large set of features have been tried by researchers. E.g.,

  • Terms frequency and different IR weighting schemes
  • Part of speech (POS) tags
  • Opinion words and phrases
  • Negations
  • Syntactic dependency

• Polarity detection:
  • Is an IMDB movie review positive or negative?
• Data: Polarity Data 2.0:
  • http://www.cs.cornell.edu/people/pabo/movie-review-data

• Pre-processing and tokenization
• Feature representation (DTM)
• Feature selection
• Classification

• How to handle negation

  • I didn’t like this movie
    vs
    
  • I really like this movie

• Which words to use?

  • Only adjectives

  • All words

    • All words turns out to work better, at least on this data

Add NOT_ to every word between negation and following punctuation:

• Using all words works well for some tasks

• Finding subsets of words may help in other tasks

  • Hand-built polarity lexicons
  • Use seeds and semi-supervised learning to induce lexicons

• Negation is important

• Sentiment: positive, negative, neutral

• Emotion: angry, sad, joyful, fearful, ashamed, proud, elated

• Disease: healthy, cold, flu

• Weather: sunny, cloudy, rain, snow

• Sentiment analysis
• Lexicon-based methods
• Learning-based methods
• Multiclass classification
• Multi-label classification

• Explicit aspects: Aspects explicitly mentioned as nouns or noun phrases in a sentence

  • “The picture quality is of this phone is great.”

• Implicit aspects: Aspects not explicitly mentioned in a sentence but are implied

  • “This car is so expensive.”
  • “This phone will not easily fit in a pocket.”
  • “Included 16MB is stingy.”

An implicit aspect should satisfy the following conditions:

• The related aspect word does not occur in the review sentence explicitly.

• The aspect can be discovered by its surrounding words (e.g. opinion words) in the review sentence.

• Trying out Chrome because Firefox keeps crashing.

  • Firefox - negative; no opinion about chrome.

  • We need to segment the sentence into clauses to decide that “crashing” only applies to Firefox(?).

• But how about these

  • I changed to Audi because BMW is so expensive.

  • I did not buy BMW because of the high price.

  • I am so happy that my iPhone is nothing like my old ugly phone.

• Conditional sentences are hard to deal with (Narayanan et al. 2009)

  • If I can find a good camera, I will buy it.

  • But conditional sentences can have opinions

    • If you are looking for a good phone, buy Nokia

• Questions are also hard to handle

  • Are there any great perks for employees?

  • Any idea how to fix this lousy Sony camera?

• Sarcastic sentences

  • What a great car, it stopped working in the second day.

• Sarcastic sentences are common in political blogs, comments and discussions.

  • They make political opinions difficult to handle