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An empirical evaluation of electronic annotation tools for Twitter data
Davy Weissenbacher1, Karen O'Connor1, Aiko T Hiraki2
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Genomics & Informatics
|July 8, 2020
Summary
No specific annotation tools exist for Twitter data in natural language processing (NLP) research. Researchers adapted existing tools like GATE and TextAE for Twitter timeline annotation, though some functionality compromises are needed.
Area of Science:
- Natural Language Processing
- Computational Linguistics
- Social Media Analysis
Background:
- Growing number of natural language processing (NLP) shared-tasks utilize Twitter data.
- Lack of dedicated annotation tools for Twitter timelines hinders research.
- Existing generic annotation tools often lack features specific to Twitter's unique data structure.
Purpose of the Study:
- To address the absence of ad-hoc annotation tools for Twitter data.
- To evaluate the suitability of existing annotation tools for Twitter timeline annotation.
- To adapt and test specific tools for the unique requirements of annotating Twitter data.
Main Methods:
- Conducted a review of 19 generic annotation tools.
- Adapted the General Architecture for Text Engineering (GATE) for Twitter timeline annotation.
- Modified TextAE (a web-based annotation tool) for Twitter data.
- Evaluated the adapted tools during the 6th edition of the BLAH workshop.
Main Results:
- No single reviewed tool fully supports all inherent information in Twitter timelines.
- GATE and TextAE were adapted, showing potential for Twitter data annotation.
- Annotators may need to compromise on certain functionalities when using adapted tools.
Conclusions:
- Existing generic annotation tools can be adapted for Twitter timeline annotation with modifications.
- Further development is needed to create comprehensive annotation tools for Twitter data.
- Adaptation of tools like GATE and TextAE offers a viable, albeit imperfect, solution for current NLP research involving Twitter.
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