TAILIEUCHUNG - Báo cáo khoa học: "Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter"

Microblogging websites such as Twitter offer a wealth of insight into a population’s current mood. Automated approaches to identify general sentiment toward a particular topic often perform two steps: Topic Identification and Sentiment Analysis. Topic Identification first identifies tweets that are relevant to a desired topic (., a politician or event), and Sentiment Analysis extracts each tweet’s attitude toward the topic. Many techniques for Topic Identification simply involve selecting tweets using a keyword search. Here, we present an approach that instead uses distant supervision to train a classifier on the tweets returned by the search. We show that distant. | Learning for Microblogs with Distant Supervision Political Forecasting with Twitter Micol Marchetti-Bowick Microsoft Corporation 475 Brannan Street San Francisco CA 94122 micolmb@ Nathanael Chambers Department of Computer Science United States Naval Academy Annapolis MD 21409 nchamber@ Abstract Microblogging websites such as Twitter offer a wealth of insight into a population s current mood. Automated approaches to identify general sentiment toward a particular topic often perform two steps Topic Identification and Sentiment Analysis. Topic Identification first identifies tweets that are relevant to a desired topic . a politician or event and Sentiment Analysis extracts each tweet s attitude toward the topic. Many techniques for Topic Identification simply involve selecting tweets using a keyword search. Here we present an approach that instead uses distant supervision to train a classifier on the tweets returned by the search. We show that distant supervision leads to improved performance in the Topic Identification task as well in the downstream Sentiment Analysis stage. We then use a system that incorporates distant supervision into both stages to analyze the sentiment toward President Obama expressed in a dataset of tweets. Our results better correlate with Gallup s Presidential Job Approval polls than previous work. Finally we discover a surprising baseline that outperforms previous work without a Topic Identification stage. 1 Introduction Social networks and blogs contain a wealth of data about how the general public views products campaigns events and people. Automated algorithms can use this data to provide instant feedback on what people are saying about a topic. Two challenges in building such algorithms are 1 identifying topic-relevant posts and 2 identifying the attitude of each post toward the topic. This paper studies distant supervision Mintz et al. 2009 as a solution to both challenges. We apply our approach to the problem of

• Báo cáo khoa học
• Learning for Microblogs
• Distant Supervision
• Political Forecasting with Twitter
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln