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Mixtures of biased sentiment analysers

Author(s)
Salter-Townshend, Michael  
Murphy, Thomas Brendan  
Uri
http://hdl.handle.net/10197/10877
Date Issued
2013-08-31
Date Available
2019-07-10T11:27:17Z
Abstract
Modelling bias is an important consideration when dealing with inexpert annotations. We are concerned with training a classifier to perform sentiment analysis on news media articles, some of which have been manually annotated by volunteers. The classifier is trained on the words in the articles and then applied to non-annotated articles. In previous work we found that a joint estimation of the annotator biases and the classifier parameters performed better than estimation of the biases followed by training of the classifier. An important question follows from this result: can the annotators be usefully clustered into either predetermined or data-driven clusters, based on their biases? If so, such a clustering could be used to select, drop or otherwise categorise the annotators in a crowdsourcing task. This paper presents work on fitting a finite mixture model to the annotators’ bias. We develop a model and an algorithm and demonstrate its properties on simulated data. We then demonstrate the clustering that exists in our motivating dataset, namely the analysis of potentially economically relevant news articles from Irish online news sources.
Type of Material
Journal Article
Publisher
Springer
Journal
Advances in Data Analysis and Classification
Volume
8
Issue
1
Start Page
85
End Page
103
Copyright (Published Version)
2013 Springer-Verlag Berlin Heidelberg
Subjects

Bias modelling

Crowdsourcing

EM algorithm

Mixture model

Sentiment analysis

DOI
10.1007/s11634-013-0150-6
Language
English
Status of Item
Peer reviewed
ISSN
1862-5347
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Insight Research Collection
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CASL Research Collection•
Mathematics and Statistics Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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