124.11.lec5

124.11.lec5 - CS 124/LINGUIST 180: From Click to edit...

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Click to edit Master subtitle style 1/10/09 Dan Jurafsky Lecture 5: Sentiment Analysis IP notice: many slides for today from Chris Potts and Janyce Wiebe , plus some from Marti Hearst and Marta Tatu
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6/1/11 Sentiment, Style, Identity Classification Identity: Authorship identification Age/gender identification Sentiment Analysis Movie: is a review positive or negative Products (new MacBook Pro) Sentiments over time (is anger increasing or decreasing?) Politics (is this editorial left or right?) Prediction (election outcomes, market trends). Will stock go up after this news report? Style/Emotion Is this conversation (or blog) friendly, aggressive, polite, flirtatious
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6/1/11 Part I: Author Identification Slide from Marti Hearst
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6/1/11 Author Identification Also called Stylometry in the humanities An example of a Classification Problem Classifiers: Decide which of N buckets to put an item in (Some classifiers allow for multiple buckets) Slide from Marti Hearst
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6/1/11 The Disputed Federalist Papers In 1787-1788, Jay, Madison, and Hamilton wrote a series of anonymous essays to convince the voters of New York to ratify the new U. S. Constitution. Scholars have consensus that: 5 authored by Jay 51 authored by Hamilton 14 authored by Madison 3 jointly by Hamilton and Madison Slide from Marti Hearst
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6/1/11 Author identification Federalist papers In 1963 Mosteller and Wallace solved the problem They identified function words as good candidates for authorships analysis Using statistical inference they concluded the author was Madison Since then, other statistical techniques have Slide from Marti Hearst
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6/1/11 Function vs. Content Words Slide from Marti Hearst High rates for “by” favor M, low favor H High rates for “from” favor M, low says little High rats for “to” favor H, low favor M
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6/1/11 Function vs. Content Words Slide from Marti Hearst No consistent pattern for “war”
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6/1/11 Federalist Papers Problem Slide from Marti Hearst Fung, The Disputed Federalist Papers: SVM Feature 
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6/1/11 Part II: Sentiment Analysis Extraction of opinions and subjective feelings from text and speech More generally, extraction of social meaning Opinions (positive or negative) on a topic or generally “Twitter mood predicts the stock market.” Social identity (Democrat, Republican, etc.) Uncertainty (students in tutoring) Annoyance and other emotions (telephone dialogues) Deception
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6/1/11 Example: Political Sentiment Two examples of classifiers Using words as features And a Naïve Bayes or SVM classifier To make a binary decision About the political stance of a text
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6/1/11 Political Sentiment Lin, Wilson, Wiebe, Hauptmann (2006) Bitterlemon.com A website designed to “contribute to mutual understanding [between Palestinians and Israelis] through the open exchange of ideas” Can we label Israeli & Palestinian perspective: 1.
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124.11.lec5 - CS 124/LINGUIST 180: From Click to edit...

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