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Semi-supervised sentiment clustering on natural language texts

Author

Listed:
  • Luca Frigau

    (University of Cagliari)

  • Maurizio Romano

    (University of Cagliari)

  • Marco Ortu

    (University of Cagliari)

  • Giulia Contu

    (University of Cagliari)

Abstract

In this paper, we propose a semi-supervised method to cluster unstructured textual data called semi-supervised sentiment clustering on natural language texts. The aim is to identify clusters homogeneous with respect to the overall sentiment of the texts analyzed. The method combines different techniques and methodologies: Sentiment Analysis, Threshold-based Naïve Bayes classifier, and Network-based Semi-supervised Clustering. It involves different steps. In the first step, the unstructured text is transformed into structured text, and it is categorized into positive or negative classes using a sentiment analysis algorithm. In the second step, the Threshold-based Naïve Bayes classifier is applied to identify the overall sentiment of the texts and to define a specific sentiment value for the topics. In the last step, Network-based Semi-supervised Clustering is applied to partition the instances into disjoint groups. The proposed algorithm is tested on a collection of reviews written by customers on Booking.com. The results have highlighted the capacity of the proposed algorithm to identify clusters that are distinct, non-overlapped, and homogeneous with respect to the overall sentiment. Results are also easily interpretable thanks to the network representation of the instances that helps to understand the relationship between them.

Suggested Citation

  • Luca Frigau & Maurizio Romano & Marco Ortu & Giulia Contu, 2023. "Semi-supervised sentiment clustering on natural language texts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1239-1257, October.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:4:d:10.1007_s10260-023-00691-4
    DOI: 10.1007/s10260-023-00691-4
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    References listed on IDEAS

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