TAILIEUCHUNG - Báo cáo khoa học: "Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification"

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. . | Biographies Bollywood Boom-boxes and Blenders Domain Adaptation for Sentiment Classification John Blitzer Mark Dredze Fernando Pereira Department of Computer and Information Science University of Pennsylvania blitzer mdredze pereria@ Abstract Automatic sentiment classification has been extensively studied and applied in recent years. However sentiment is expressed differently in different domains and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers focusing on online reviews for different types of products. First we extend to sentiment classification the recently-proposed structural correspondence learning SCL algorithm reducing the relative error due to adaptation between domains by an average of 30 over the original SCL algorithm and 46 over a supervised baseline. Second we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains. 1 Introduction Sentiment detection and classification has received considerable attention recently Pang et al. 2002 Turney 2002 Goldberg and Zhu 2004 . While movie reviews have been the most studied domain sentiment analysis has extended to a number of new domains ranging from stock message boards to congressional floor debates Das and Chen 2001 Thomas et al. 2006 . Research results have been 440 deployed industrially in systems that gauge market reaction and summarize opinion from Web pages discussion boards and blogs. With such widely-varying domains researchers and engineers who build sentiment classification systems need to collect and curate data for each new domain they encounter. Even in the case of market analysis if automatic sentiment classification were to be used across a wide range of domains the .

• Báo cáo khoa học
• Biographies
• Bollywood
• Boom boxes and Blenders
• Domain Adaptation
• Sentiment Classification
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Winston Churchill
• Frank Capra
• Benito Mussolini
• Jeannette Rankin
• Franz Jaggerstatter
• The Incas
• Primary Sources
• Inca culture
• Sacred Calendar
• Number System
• Sarah Winnemucca
• John Augustus
• Annie Oakley
• Andrew Jackson
• Geronimo
• Brief Biographies
• Seth Curtis Beach
• the Puritans
• Biography
• autobiography
• History
• juliet
• romeo
• Biographies
• Novels
• Memoir
• sCreateSpace
• The Committee’s Task
• Community Resilience
• Managing Risk
• Conceptual Framework
• Levee Performance
• Committee Biographies
• Resilient Community
• runaway
• roman
• story
• A
• Braithwaite
• Boy
• The Lady and Sada San
• Octavia Hill
• Henry James
• Eugene Pickering
• Tom Swift in Captivity
• Knowledge for the outdoors
• The walker papers
• A gift from malla
• The Blue Fairy Book
• Assurer Le Bien être Des Familles
• Before the storm
• Civilians And Soldiers
• Jarvis Get Out of Our Skies
• Francia
• Fyodor Mikhailovich Dostoyevsky
• Weiße Nachte
• Gjimnazistet e semure
• muahammat