TAILIEUCHUNG - Báo cáo khoa học: "Experimenting with Distant Supervision for Emotion Classification"

We describe a set of experiments using automatically labelled data to train supervised classifiers for multi-class emotion detection in Twitter messages with no manual intervention. By cross-validating between models trained on different labellings for the same six basic emotion classes, and testing on manually labelled data, we conclude that the method is suitable for some emotions (happiness, sadness and anger) but less able to distinguish others; and that different labelling conventions are more suitable for some emotions than others. . | Experimenting with Distant Supervision for Emotion Classification Matthew Purver and Stuart Battersby Interaction Media and Communication Group Chatterbox Analytics School of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road London E1 4NS UK stuart@ Abstract We describe a set of experiments using automatically labelled data to train supervised classifiers for multi-class emotion detection in Twitter messages with no manual intervention. By cross-validating between models trained on different labellings for the same six basic emotion classes and testing on manually labelled data we conclude that the method is suitable for some emotions happiness sadness and anger but less able to distinguish others and that different labelling conventions are more suitable for some emotions than others. 1 Introduction We present a set of experiments into classifying Twitter messages into the six basic emotion classes of Ekman 1972 . The motivation behind this work is twofold firstly to investigate the possibility of detecting emotions of multiple classes rather than purely positive or negative sentiment in such short texts and secondly to investigate the use of distant supervision to quickly bootstrap large datasets and classifiers without the need for manual annotation. Text classification according to emotion and sentiment is a well-established research area. In this and other areas of text analysis and classification recent years have seen a rise in use of data from online sources and social media as these provide very large often freely available datasets see . Eisenstein et al. 2010 Go et al. 2009 Pak and Paroubek 2010 amongst many others . However one of the challenges this poses is that of data annotation given very large amounts of data often consisting of very short texts written in unconventional style and without accompanying metadata audio video signals or access to the author for .

• Báo cáo khoa học
• Experimenting with Distant Supervision
• Emotion Classification
• Matthew Purver
• 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
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• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln