807 - text analytics massimo poesio lecture 4: sentiment analysis (aka opinion mining)

807 - TEXT ANALYTICS

Massimo Poesio

Lecture 4: Sentiment analysis (aka Opinion Mining)

FACTS AND OPINIONS

• Two main types of textual information on the Web: FACTS and OPINIONS

• Current search engines search for facts (assume they are true)– Facts can be expressed with topic keywords.

THERE IS PLENTY OF OPINIONS IN THE WEB

SENTIMENT ANALYSIS

• (also known as opinion mining)• Attempts to identify the opinion/sentiment

that a person may hold towards an object

Sentiment Analysis

Positive

Negative

Neutral

Components of an opinion

• Basic components of an opinion:– Opinion holder: The person or organization that

holds a specific opinion on a particular object.– Object: on which an opinion is expressed– Opinion: a view, attitude, or appraisal on an object

from an opinion holder.

SENTIMENT ANALYSIS GRANULARITY

• At the document (or review) level:– Task: sentiment classification of reviews– Classes: positive, negative, and neutral– Assumption: each document (or review) focuses on a single

object (not true in many discussion posts) and contains opinion from a single opinion holder.

DOCUMENT-LEVEL SENTIMENT ANALYSIS EXAMPLE

SENTIMENT ANALYSIS GRANULARITY

• At the document (or review) level:– Task: sentiment classification of reviews– Classes: positive, negative, and neutral– Assumption: each document (or review) focuses on a single

object (not true in many discussion posts) and contains opinion from a single opinion holder.

• At the sentence level:– Task 1: identifying subjective/opinionated sentences

• Classes: objective and subjective (opinionated)– Task 2: sentiment classification of sentences

• Classes: positive, negative and neutral.• Assumption: a sentence contains only one opinion; not true in many

cases.• Then we can also consider clauses or phrases.

SENTENCE-LEVEL SENTIMENT ANALYSIS EXAMPLE

Id: Abc123 on 5-1-2008 “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, …”

SENTENCE-LEVEL SENTIMENT ANALYSIS

Id: Abc123 on 5-1-2008 “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, …”

SENTENCE-LEVEL SENTIMENT ANALYSIS

Id: Abc123 on 5-1-2008 “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, …”

SENTIMENT ANALYSIS GRANULARITY

• At the feature level:– Task 1: Identify and extract object features that

have been commented on by an opinion holder (e.g., a reviewer).

– Task 2: Determine whether the opinions on the features are positive, negative or neutral.

– Task 3: Group feature synonyms.• Produce a feature-based opinion summary of multiple

reviews.

SENTIMENT ANALYSIS GRANULARITY

• At the feature level:– Task 1: Identify and extract object features that have been

commented on by an opinion holder (e.g., a reviewer).– Task 2: Determine whether the opinions on the features

are positive, negative or neutral.– Task 3: Group feature synonyms.

• Produce a feature-based opinion summary of multiple reviews.

• Opinion holders: identify holders is also useful, e.g., in news articles, etc, but they are usually known in the user generated content, i.e., authors of the posts.

FEATURE-LEVEL SENTIMENT ANALYSIS

ENTITY AND ASPECT (Hu and Liu, 2004; Liu, 2006)

OPINION TARGET

A DEFINITION OF OPINION (Liu, Ch. in NLP handbook, 2010)

SENTIMENT ANALYSIS: THE TASK

Applications• Businesses and organizations:

– product and service benchmarking.– market intelligence.– Business spends a huge amount of money to find consumer

sentiments and opinions.• Consultants, surveys and focused groups, etc

• Individuals: interested in other’s opinions when – purchasing a product or using a service, – finding opinions on political topics

• Ads placements: Placing ads in the user-generated content– Place an ad when one praises a product.– Place an ad from a competitor if one criticizes a product.

• Opinion retrieval/search: providing general search for opinions.

DOCUMENT-LEVEL SENTIMENT ANALYSIS

DOCUMENT-LEVEL SENTIMENT ANALYSIS

DOCUMENT-LEVEL SENTIMENT ANALYSIS = TEXT CLASSIFICATION

ASSUMPTIONS AND GOALS

LEXICON-BASED APPROACHES

• Use sentiment and subjectivity lexicons• Rule-based classifier– A sentence is subjective if it has at least two words

in the lexicon– A sentence is objective otherwise

SUPERVISED CLASSIFICATION

• Treat sentiment analysis as a type of classification• Use corpora annotated for subjectivity and/or

sentiment• Train machine learning algorithms:– Naïve bayes– Decision trees– SVM – …

• Learn to automatically annotate new text

TYPICAL SUPERVISED APPROACH

FEATURES FOR SUPERVISED DOCUMENT-LEVEL SENTIMENT ANALYSIS

• A large set of features have been tried by researchers (see e.g., work here at Essex by Roseline Antai)– Terms frequency and different IR weighting

schemes as in other work on classification– Part of speech (POS) tags– Opinion words and phrases– Negations– Syntactic dependency

EASIER AND HARDER PROBLEMS

• Tweets from Twitter are probably the easiest– short and thus usually straight to the point

• Reviews are next – entities are given (almost) and there is little noise

• Discussions, comments, and blogs are hard. – Multiple entities, comparisons, noisy, sarcasm, etc

ASPECT-BASED SENTIMENT ANALYSIS

• Sentiment classification at the document or sentence (or clause) levels are useful, but do not find what people liked and disliked.

• They do not identify the targets of opinions, i.e., ENTITIES and their ASPECTS

• Without knowing targets, opinions are of limited use.

ASPECT-BASED SENTIMENT ANALYSIS

• Much of the research is based on online reviews• For reviews, aspect-based sentiment analysisis

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.

BRIEF DIGRESSION

• 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.– “iPhone is better than Blackberry”

Find entities (entity set expansion)

• Although similar, it is somewhat different from the traditional named entity recognition (NER). (See next lectures)

• 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,

Feature/Aspect extraction

• May extract 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 “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)

Aspect extraction using dependency grammar

RESOURCES FOR SENTIMENT ANALYSIS

• Lexicons• General Inquirer (Stone et al., 1966)• OpinionFinder lexicon (Wiebe & Riloff,

2005)• SentiWordNet (Esuli & Sebastiani, 2006)

• Annotated corpora• Used in statistical approaches (Hu

& Liu 2004, Pang & Lee 2004)• MPQA corpus (Wiebe et. al, 2005)

• Tools • Algorithm based on minimum

cuts (Pang & Lee, 2004) • OpinionFinder (Wiebe et. al, 2005)

Lexical resources for Sentiment and Subjectivity Analysis

Overview

Sentiment (or opinion) lexica

Sentiment lexica

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Sentiment-bearing words

ICWSM 2008

• Adjectives Hatzivassiloglou & McKeown 1997, Wiebe 2000, Kamps & Marx 2002, Andreevskaia & Bergler 2006

– positive: honest important mature large patient

• Ron Paul is the only honest man in Washington. • Kitchell’s writing is unbelievably mature and is only

likely to get better. • To humour me my patient father agrees yet again to my

choice of film

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Negative adjectives

ICWSM 2008

• Adjectives– negative: harmful hypocritical inefficient insecure• It was a macabre and hypocritical circus. • Why are they being so inefficient ? bjective: curious,

peculiar, odd, likely, probably

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Subjective adjectives

ICWSM 2008

• Adjectives – Subjective (but not positive or negative

sentiment): curious, peculiar, odd, likely, probable• He spoke of Sue as his probable successor.• The two species are likely to flower at different times.

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Other words

ICWSM 2008

• Other parts of speech Turney & Littman 2003, Riloff, Wiebe & Wilson 2003, Esuli & Sebastiani 2006

– Verbs• positive: praise, love• negative: blame, criticize• subjective: predict

– Nouns• positive: pleasure, enjoyment• negative: pain, criticism• subjective: prediction, feeling

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Phrases

ICWSM 2008

• Phrases containing adjectives and adverbs Turney 2002, Takamura, Inui & Okumura 2007

– positive: high intelligence, low cost– negative: little variation, many troubles

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Sentiment lexica

ICWSM 2008

• Human-created– WordNet Affect

• Semi-automatic– SentiWordNet 3.0

• Fully automatic– SenticNet 2.0

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(Semi) Automatic creation of sentiment lexica

ICWSM 2008

• Find relevant words, phrases, patterns that can be used to express subjectivity

• Determine the polarity of subjective expressions

FINDING POLARITY IN CORPORA USING PATTERNS

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USING PATTERNS

ICWSM 2008

• Lexico-syntactic patterns Riloff & Wiebe 2003

• way with <np>: … to ever let China use force to have its way with …

• expense of <np>: at the expense of the world’s security and stability

• underlined <dobj>: Jiang’s subdued tone … underlined his desire to avoid disputes …

DICTIONARY-BASED METHODS

SEMI-SUPERVISED LEARNING(Esuti and Sebastiani, 2005)

Corpora for Sentiment and Subjectivity Analysis

Overview

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MPQA

ICWSM 2008

• MPQA: www.cs.pitt.edu/mqpa/databaserelease (version 2)

• English language versions of articles from the world press (187 news sources)

• Also includes contextual polarity annotations (later)

• Themes of the instructions:– No rules about how particular words should be annotated.

– Don’t take expressions out of context and think about what they could mean, but judge them as they are used in that sentence.

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Definitions and Annotation Scheme

ICWSM 2008

• Manual annotation: human markup of corpora (bodies of text)

• Why? – Understand the problem– Create gold standards (and training data)

Wiebe, Wilson, Cardie LRE 2005Wilson & Wiebe ACL-2005 workshopSomasundaran, Wiebe, Hoffmann, Litman ACL-2006 workshopSomasundaran, Ruppenhofer, Wiebe SIGdial 2007Wilson 2008 PhD dissertation

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Overview

ICWSM 2008

• Fine-grained: expression-level rather than sentence or document level

• Annotate – Subjective expressions– material attributed to a source, but presented

objectively

OTHER CORPORA

• The Movie Review data created by Pang and Lee– http://www.cs.cornell.edu/People/pabo/movie-review-data/

• The Semeval 2007 and 2014 (sentiment analysis in Twitter) shared tasks data– http://alt.qcri.org/semeval2014/task9/

• The Kaggle 2014 competition for Sentiment Analysis on movie reviews– https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews

TOOLS

OPINE

OPINION SUMMARIES

GOOGLE PRODUCTS

READINGS

• Bo Pang & Lillian Lee, 2008 – Opinion Mining and Sentiment Analysis – Foundations and Trends in Information Retrieval, v. 2, 1-2– On the website

ACKNOWLEDGMENTS

• Some slides borrowed from– Janyce Wiebe’s tutorials– Bing Liu’s tutorials– Ronen Feldman’s IJCAI 2013 tutorial