Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter

Zach Wood-Doughty, Praateek Mahajan, Mark Dredze

Abstract
Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available.
Anthology ID:
W18-1108
Volume:
Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media
Month:
June
Year:
2018
Address:
New Orleans, Louisiana, USA
Editors:
Malvina Nissim, Viviana Patti, Barbara Plank, Claudia Wagner
Venue:
PEOPLES
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
56–61
Language:
URL:
https://aclanthology.org/W18-1108
DOI:
10.18653/v1/W18-1108
Bibkey:
Cite (ACL):
Zach Wood-Doughty, Praateek Mahajan, and Mark Dredze. 2018. Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter. In Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pages 56–61, New Orleans, Louisiana, USA. Association for Computational Linguistics.
Cite (Informal):
Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter (Wood-Doughty et al., PEOPLES 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/W18-1108.pdf
Code
 mdredze/demographer
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