1. 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

2. 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

3. Overview Introduction Coarse-grained methods Fine-grained methods Resources Advanced topics: recent research from LK

4. 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.”

5. Direct applications Consumer information Quickly surveying evaluations from other consumers Conversely, companies may survey what customers think Social and political sciences Surveying popular opinion on contentious issues Track the development of opinion over time Measure the effect of some event on opinions

6. Indirect applications Retrieval systems given a topic, identify documents that express attitudes toward this topic Question-answering systems Obvious: What does X think about Y? Also: Filtering out opinionated text before returning answers

7. A note on terminology Opinion extraction/analysis/mining etc Sentiment analysis/extraction Subjectivity analysis/extraction Etc etc etc

8. Coarse-grained Opinion Extraction Classification of fairly large units of text (e.g. documents) Examples: Distinguish editorials from “objective” news text Given a review (product, movie, restaurant, …), predict the number of stars

9. 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 negative really: 0.625 positive boring; 0.25 negative

10. Classification using machine learning Coarse-grained opinion extraction is a type of text categorization Categorize the text As factual or opinionated As positive or negative (or the number of stars) We may then obviously apply classical text categorization methods (Pang and Lee, 2002)

11. 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, …)

12. 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

13. 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) domain adaptation 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)

14. 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 appliances Baseline 0.74, upper bound 0.88 With domain adaptation: 0.81

15. 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.”

16. Common subtasks Mark up opinion expressions in the text Label expressions with polarity values Find opinion holders for the opinions Find the topics (targets) of the opinions

17. Opinion Expressions An opinion expression is a piece of text that allows us to conclude that some entity has some opinion – a private state The MPQA corpus (Wiebe et al., 2005) defines two main types of expressions: Direct-subjective: typically emotion, communication, and categorization verbs Expressive subjective: typically qualitative adjectives and “loaded language”

18. Examples of opinion expressions I [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.

19. Opinion Holders For every opinion expression, there is an associated opinion holder. Also annotated in the MPQA Our system finds three types of holders: Explicitly mentioned holders in the same sentence The writer of the text Implicit holder, such as in passive sentences (“he was widely condemned”)

20. Examples of opinion holders Explicitly 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.

21. Nested structure of opinion scopes Sharon [insinuated] ESE+DSE that Arafat [hated] DSE Israel. Writer: negative opinion on Sharon Sharon: negative opinion on Arafat Arafat: negative opinion on Israel The MPQA corpus annotates the nested structure of opinion/holder scopes Our system does not take the nesting into account

22. 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.

23. 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)

24. Example

25. 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

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

27. 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

28. Resources: Collections Pang: Movie reviews (pos/neg) http://www.cs.cornell.edu/people/pabo/movie-review-data Liu: Product features http://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

29. Resources: Lexicons MPQA lexicon http://www.cs.pitt.edu/mpqa/lexiconrelease/collectinfo1.html SentiWordNet http://sentiwordnet.isti.cnr.it

30. Advanced topic 1: Opinion extraction with an interaction model Previous work used bracketing methods with local features and Viterbi decoding In a sequence labeler using local features only, the model can’t take into account the interactions between opinion expressions Opinions 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

31. 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

32. 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 good Contrast: Zürich is beautiful but its restaurants are expensive

33. Learning the Interaction model We need a new model based on interactions between opinions We 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)

34. 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.

35. Evaluation (Johansson and Moschitti 2010a, 2010b, 2011) Opinion markupF-measure Baseline53.8 Reranked58.5 Holder identificationF-measure Baseline50.8 Extended54.2 Markup + polarityF-measure Baseline45.7 Extended49.7

36. 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)

37. Extraction of Feature Evaluations We built a system that used features derived from the MPQA-style opinion expressions We compared with two baselines: Simple baseline using local features only Stronger baseline using sentiment lexicon

38. Extraction of Feature Evaluations

39. References E. 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.

40. References R. 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.

41. 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.