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UIC at TREC 2006: Blog Track Wei Zhang Clement Yu Department of Computer Science University of Illinois at Chicago
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Summary Overview of the opinion retrieval Relevant document retrieval Opinion relevant document retrieval Opinion system Subjective/objective training data Feature extraction Subjectivity classifier Opinion document ranking
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Opinion Document Retrieval Query Opinion Documents Document Space Relevant Documents Opinion Relevant Documents
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Opinion Document Retrieval Relevant documents – an IR approach Opinion relevant documents – a classification approach
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Relevant Document Retrieval The UIC IR system in TREC 2005 Robust Track Without WSD and adding synonyms/hyponyms Phrase recognition –Proper name, dictionary phrase –Simple phrase, complex phrase Query expansion –pseudo relevant feedback, Wikipedia, Web Document-query similarity –Phrase similarity and term similarity
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Opinion Relevant Document Retrieval Retrieved documents a document Opinion sentences For a documentary, it carried just about no information. … … another bad thing about march of the penguins - I totally agree.... " march of the penguins," which was excellent yet really pretty disturbing … opinion relevant document
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The Opinions Opinions are query dependent – food automobile – Should be learned and tested depending on queries – Should be analyzed within the sentences
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Opinion System Overview query Rateitall.com Subjective sentences Feature Extraction SVM classifier Retrieved Documents Opinion Relevant Documents Wikipedia.org Objective sentences Opinion Documents Opinion - query connection Re-rank Final answers
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The Objective Sentences Wikipedia.org pages as primary source – every sentence is objective – multiple pages for multiple phrases –Web pages as secondary source – from web search engine – restriction: -comment -review, -”I think”
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The Subjective Sentences Rateitall.com pages as primary source – every comment sentence is subjective Web pages as secondary source – from web search engine – restriction: +comment, +review, +”I think”.
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The Featured Terms Use unigrams and bigrams Chi-square test –to test the hypothesis that a term t is distributed unevenly in the objective text set and the subjective text set
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The Sentence Classifier Support Vector Machine sentence classifier Objective sentencesSubjective sentences Featured terms SVM Training Featured term vector representation SVM classifier
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Find the Opinion Documents A retrieved document that contains at least one opinion sentence –Split document to sentences –Test each sentence by the classifier SVM classifier Document Sentence 1 … Label 1:objective … Sentence 2 Sentence n Label 2:subjective Label n:objective
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Find the Opinion Relevant Documents A retrieved document that contains at least one opinion “relevant” sentence –query terms in or near a opinion sentence queryopinion sentence document text window
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Rank the Opinion Relevant Documents Strategy 1 –Use the document retrieval ranking –Remove documents that does not have opinion relevant sentence Sim(D, Q): query-doc similarity I(D, Q) = 1 if D contains opinion relevant sentence = 0 otherwise
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Rank the Opinion Relevant Documents Strategy 2 –Calculate a document opinion score OS(D): opinion sentence set of document D Score classification (s): score of the opinion sentence s from the SVM classifier Relevant(s, Q): 1 if s is a opinion relevant sentence, 0 otherwise
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Blog Track Results Run IDStrategyMAPGMAPR-PrecP@10 UICSR1.1636.0921.2522.4380 UICST2.1885.1083.2771.5120
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Thanks! and Questions?
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