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LINGUISTICA GENERALE E COMPUTAZIONALE
SENTIMENT ANALYSIS
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– 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
SENTIMENT ANALYSIS IN PYTHON
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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Creating sentiment lexica
ICWSM 2008
• Humans
• Semi-automatic
• Fully automatic
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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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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
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Corpus
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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Gold Standards
ICWSM 2008
• Derived from manually annotated data• Derived from “found” data (examples): – Blog tags Balog, Mishne, de Rijke EACL 2006
– Websites for reviews, complaints, political arguments• amazon.com Pang and Lee ACL 2004
• complaints.com Kim and Hovy ACL 2006
• bitterlemons.com Lin and Hauptmann ACL 2006 • Word lists (example):– General Inquirer Stone et al. 1996
SENTIMENT ANALYSIS IN NLTK
• See 6.1
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