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Mixtures of biased sentiment analysers

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  • Michael Salter-Townshend
  • Thomas Murphy

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. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Michael Salter-Townshend & Thomas Murphy, 2014. "Mixtures of biased sentiment analysers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 85-103, March.
    • Handle: RePEc:spr:advdac:v:8:y:2014:i:1:p:85-103
    DOI: 10.1007/s11634-013-0150-6
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    References listed on IDEAS

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    1. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    2. Govaert, Gérard & Nadif, Mohamed, 2008. "Block clustering with Bernoulli mixture models: Comparison of different approaches," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3233-3245, February.
    3. Hathaway, Richard J., 1986. "Another interpretation of the EM algorithm for mixture distributions," Statistics & Probability Letters, Elsevier, vol. 4(2), pages 53-56, March.
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    Cited by:

    1. Anna Calissano & Simone Vantini & Marika Arena, 2020. "Monitoring rare categories in sentiment and opinion analysis: a Milan mega event on Twitter platform," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 787-812, December.

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