Mixtures of biased sentiment analysers
Files in This Item:
|Mixtures of biased sentiment analysers.pdf||255.83 kB||Adobe PDF||Download|
|Title:||Mixtures of biased sentiment analysers||Authors:||Salter-Townshend, Michael; Murphy, Thomas Brendan||Permanent link:||http://hdl.handle.net/10197/10877||Date:||31-Aug-2013||Online since:||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||Keywords:||Bias modelling; Crowdsourcing; EM algorithm; Mixture model; Sentiment analysis||DOI:||10.1007/s11634-013-0150-6||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.