Extraction of Opinions on the Web

Richard Johansson

Presentation at the LK summer school

August 31, 2011

Computer Science and Engineering Department

University of Trento

Email: johansson@disi.unitn.it

Funded by EU FP7: LivingKnowledge and EternalS

Personal Background

Defended doctoral dissertation in December 2008 at Lund University, Sweden

I now work as a postdoctoral researcher at the University of Trento, Italy

PhD work focused on NLP tasks such as syntactic parsing and shallow-semantic extraction

Postdoc work on the applications of these methods in areas such as opinion extraction

Overview

Introduction

Coarse-grained methods

Fine-grained methods

Resources

Advanced topics: recent research from LK

Introduction

Extraction of opinions expressed on the web is a task with many practical applications

“give me all positive opinions expressed by Sarkozy last week”“what is the overall perception (positive/negative) on the New Start treaty?”

“Vaclav Klaus expressed his [disapproval] of the treaty while French Prime Minister Sarkozy [supported] it.”

Direct applications

Consumer informationQuickly surveying evaluations from other consumersConversely, companies may survey what customers think

Social and political sciencesSurveying popular opinion on contentious issuesTrack the development of opinion over timeMeasure the effect of some event on opinions

Indirect applications

Retrieval systemsgiven a topic, identify documents that express attitudes toward this topic

Question-answering systemsObvious: What does X think about Y?Also: Filtering out opinionated text before returning answers

A note on terminology

Opinion extraction/analysis/mining etc

Sentiment analysis/extraction

Subjectivity analysis/extraction

Etc etc etc

Coarse-grained Opinion Extraction

Classification of fairly large units of text (e.g. documents)

Examples:Distinguish editorials from “objective” news textGiven a review (product, movie, restaurant, …), predict the number of stars

Lexicon-based Methods

Simplest solution: count “positive” and “negative” words listed in some lexicon

Also weighted

Lexicons may be generic or domain-specific

Example (with SentiWordNet, first sense):“This movie is awful with really boring actors”

awful: 0.875 negativereally: 0.625 positiveboring; 0.25 negative

Classification using machine learning

Coarse-grained opinion extraction is a type of text categorization

Categorize the textAs factual or opinionatedAs positive or negative (or the number of stars)

We may then obviously apply classical text categorization methods (Pang and Lee, 2002)

Classification using machine learning

Represent a document using a bag of words representation (i.e. a histogram)

Optionally, add extra features for words that appear in some lexicon

Apply some machine learning method to learn to separate the documents into classes (e.g. SVM, MaxEnt, Naïve Bayes, …)

But the context…

“The price is high – I saw many cheaper options elsewhere”

In practice, expressions of opinion are highly context-sensitive: Unigram (BOW or lexicon) models may run into difficulties

Possible solutions:Bigrams, trigrams, …Syntax-based representations Very large feature spaces: feature selection needed

Domain Adaptation

Problem: an opinion classifier trained on one collection (e.g. reviews of hotels) may not perform well on a collection from a different domain (e.g. reviews of cars)

We may apply domain adaptation methods (Blitzer et al., 2007, inter alia)

Similar methods may be applied for lexicon-based opinion classifiers (Jijkoun et al., 2010)

Structural Correspondence Learning (Blitzer et al., 2007)

Idea:Some pivot features generalize across domains (e.g. “good”, “awful”)Some features are completely domain-specific (“plastic”, “noisy”, “dark”) Find correlations between pivot and domain-specific

Example experiment:DVD movies -> kitchen appliancesBaseline 0.74, upper bound 0.88With domain adaptation: 0.81

Fine-grained Opinion Extraction

We may want to pose more complex queries:“give me all positive opinions expressed by Sarkozy last week”“what is the overall perception (positive/negative) on the New Start treaty?”“what is good and what is bad about the new Canon camera?”

“Vaclav Klaus expressed his [disapproval] of the treaty while French Prime Minister Sarkozy [supported] it.”

Common subtasksMark up opinion expressions in the textLabel expressions with polarity valuesFind opinion holders for the opinionsFind the topics (targets) of the opinions

Opinion ExpressionsAn opinion expression is a piece of text that allows us to conclude that some entity has some opinion – a private stateThe MPQA corpus (Wiebe et al., 2005) defines two main types of expressions:

Direct-subjective: typically emotion, communication, and categorization verbsExpressive subjective: typically qualitative adjectives and “loaded language”

Examples of opinion expressionsI [love]DSE this [fantastic]ESE conference.[However]ESE, it is becoming [rather

fashionable]ESE to [exchange harsh words]DSE

with each other [like kids]ESE.The software is [not so easy]ESE to use.

Opinion HoldersFor every opinion expression, there is an associated opinion holder.Also annotated in the MPQAOur system finds three types of holders:

Explicitly mentioned holders in the same sentenceThe writer of the textImplicit holder, such as in passive sentences (“he was widely condemned”)

Examples of opinion holdersExplicitly mentioned holder: I [love]DSE this [fantastic]ESE conference.Writer (red) and implicit (green): [However]ESE, it is becoming [rather fashionable]ESE to [exchange harsh words]DSE with each other [like kids]ESE.

Nested structure of opinion scopes

Sharon [insinuated]ESE+DSE that Arafat [hated]DSE Israel.

Writer: negative opinion on SharonSharon: negative opinion on ArafatArafat: negative opinion on Israel

The MPQA corpus annotates the nested structure of opinion/holder scopesOur system does not take the nesting into account

Opinion polarities

Every opinion expression has a polarity: positive, negative, or neutral (for non-evaluative opinions)

I [love] this [fantastic] conference.[However], it is becoming [rather fashionable] to [exchange harsh words] with each other [like kids].The software is [not so easy] to use.

Tagging Opinion Expressions

The obvious approach – which we used as a baseline – would be a standard sequence labeler with Viterbi decoding.

Sequence labeler using word, POS tag, and lemma features in a sliding window

Can also use prior polarity/intensity features derived from the MPQA subjectivity lexicon.

This was the approach by Breck et al. (2007)

Example

Extracting Opinion Holders

For opinion holder extraction, we trained a classifier based on techniques common in semantic role labeling

Applies to the noun phrases in a sentence

A separate classifier detects implicit and writer opinion holders

At prediction time, the opinion holder candidate with the maximal score is selected

Syntactic structure and semantic rolesWe used the LTH syntactic/semantic parser to extract features (Johansson and Nugues, 2008)Outputs dependency parse trees and semantic role structures

Classifying Expression Polarity

Given an opinion expression, assign a polarity label (Positive, Neutral, Negative)

SVM classifier with BOW representation of the expression and its context, lexicon features

Resources: Collections

Pang: Movie reviews (pos/neg)http://www.cs.cornell.edu/people/pabo/movie-review-data

Liu: Product featureshttp://www.cs.uic.edu/~liub/FBS/CustomerReviewData.zip

Dredze: Multi-domain product reviews (pos/neg)http://www.cs.jhu.edu/~mdredze/datasets/sentiment

MPQA: Fine-grained annotation: expressions, holder, polarities, intensities, holder coreference

http://www.cs.pitt.edu/mpqa/databaserelease

Resources: Lexicons

MPQA lexiconhttp://www.cs.pitt.edu/mpqa/lexiconrelease/collectinfo1.html

SentiWordNethttp://sentiwordnet.isti.cnr.it

Advanced topic 1: Opinion extraction with an interaction model

Previous work used bracketing methods with local features and Viterbi decodingIn a sequence labeler using local features only, the model can’t take into account the interactions between opinion expressionsOpinions tend to be structurally close in the sentence, and occur in patterns, for instance

Verb of categorization dominating evaluation:He denounced as a human rights violation …Discourse connections:Zürich is beautiful but its restaurants are expensive

Interaction (opinion holders)For verbs of evaluation/categorization, opinion holder extraction is fairly easy (basically SRL)They may help us find the holder of other opinions expressed in the sentence:

He denounced as a human rights violation …This is a human rights violation …

Linguistic structure may be useful to determine whether two opinions have the same holder

Interaction (polarity)The relation between opinion expressions may influence polarity:

He denounced as a human rights violation …

Discourse relations are also important:Expansion:

Zürich is beautiful and its restaurants are goodContrast:

Zürich is beautiful but its restaurants are expensive

Learning the Interaction modelWe need a new model based on interactions between opinionsWe use a standard linear model:

We decompose the feature representation:

But: Exact inference in a model with interactions is intractable (can be reduced to weighted CSP)

Approximate inference

Apply a standard Viterbi-based sequence labeler based on local context features but no structural interaction features.

Generate a small candidate set of size k.

Generate opinion holders/polarities for every proposed opinion expression.

Apply a reranker using interaction features – which can be arbitrarily complex – to pick the top candidate from the candidate set.

Evaluation

(Johansson and Moschitti 2010a, 2010b, 2011)

Opinion markup F-measureBaseline 53.8Reranked 58.5

Holder identification F-measureBaseline 50.8Extended 54.2

Markup + polarity F-measureBaseline 45.7Extended 49.7

Advanced topic 2: Extraction of Feature Evaluations

Extraction of evaluations of product features (Hu and Liu, 2004)

“This player boasts a decent size and weight, a relatively-intuitive navigational system that categorizes based on id3 tags, and excellent sound”

size +2, weight +2, navigational system +2, sound +2

We used only the signs (positive/negative)

Extraction of Feature EvaluationsWe built a system that used features derived from the MPQA-style opinion expressionsWe compared with two baselines:

Simple baseline using local features onlyStronger baseline using sentiment lexicon

Extraction of Feature Evaluations

ReferencesE. Breck, Y. Choi, C. Cardie. Identifying expressions of opinion in context. Proc.

IJCAI 2007.

J. Blitzer, M. Dredze, F. Pereira. Biographies, Bollywood, Boom-boxes and Blenders: Domain adaptation for sentiment classification. Proc. ACL 2007.

Y. Choi, C. Cardie. Hierarchical sequential learning for extracting opinions and their attributes. Proc. ACL 2010.

M. Hu, B. Liu. Mining opinion features in customer reviews. Proc. AAAI-2004.

V. Jijkoun, M. de Rijke, W. Weerkamp. Generating focused topic-specific sentiment lexicons. Proc. ACL-2010.

R. Johansson, A. Moschitti. Syntactic and semantic structure for opinion expression detection. Proc. CoNLL-2010.

R. Johansson, A. Moschitti. Reranking models in fine-grained opinion analysis. Proc. Coling-2010.

ReferencesR. Johansson, A. Moschitti. Extracting opinion expressions and their polarities –

exploration of pipelines and joint models. Proc. ACL-2011.

R. Johansson, P. Nugues. Dependency-based syntactic–semantic analysis with PropBank and NomBank. Proc. CoNLL-2008.

B. Pang, L. Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proc. ACL-2004.

S. Somasundaran, G. Namata, J. Wiebe, L. Getoor. Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. Proc. EMNLP-2009.

J. Wiebe, T. Wilson, C. Cardie. Annotating expressions of opinions and emotions in language. LRE, 39(2-3), 2005.

Acknowledgements

We have received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under the following grants:

Grant 231126: LivingKnowledge – Facts, Opinions and Bias in Time, Grant 247758:Trustworthy Eternal Systems via Evolving Software, Data and Knowledge (EternalS).

We would also like to thank Eric Breck and Yejin Choi for explaining their results and experimental setup.