Keywords
Twitter, named entity recognition, classifier, freebase
Abstract
Algorithms for classifying pre-tagged person entities in tweets into one of eight profession categories are presented. A classifier using a semi-supervised learning algorithm that takes into consideration the local context surrounding the entity in the tweet, hash tag information, and topic signature scores is described. In addition to the classifier, this research investigates two dictionaries containing the professions of persons. These two dictionaries are used in their own classification algorithms which are independent of the classifier. The method for creating the first dictionary dynamically from the web and the algorithm that accesses this dictionary to classify a person into one of the eight profession categories are explained next. The second dictionary is freebase, an openly available online database that is maintained by its online community. The algorithm that uses freebase for classifying a person into one of the eight professions is described. The results also show that classifications made using the automated constructed dictionary, freebase, or the classifier are all moderately successful. The results also show that classifications made with the automated constructed person dictionary are slightly more accurate than classifications made using freebase. Various hybrid methods, combining the classifier and the two dictionaries are also explained. The results of those hybrid methods show significant improvement over any of the individual methods.
Notes
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Graduation Date
2013
Semester
Summer
Advisor
Gomez, Fernando
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Department
Electrical Engineering and Computer Science
Degree Program
Computer Science
Format
application/pdf
Identifier
CFE0004858
URL
http://purl.fcla.edu/fcla/etd/CFE0004858
Language
English
Release Date
August 2013
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
Subjects
Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic
STARS Citation
Hall, Abraham, "Using Freebase, An Automatically Generated Dictionary, And A Classifier To Identify A Person's Profession In Tweets" (2013). Electronic Theses and Dissertations. 2636.
https://stars.library.ucf.edu/etd/2636