A semi-supervised clustering method to extract information from the electronic Word Of Mouth

Giulia Contu

;Luca Frigau;Maurizio Romano;Marco Ortu

2022-01-01

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Abstract

In time, electronic Word Of Mouth has become a resource to support the decision-making process. Different techniques have been proposed to extract information from online textual data. We propose a semi-supervised clustering model able to identify clusters homogeneous with respect to the overall sentiment of the analyzed texts. The model is built by combing Sentiment Analysis, and Network-based Semi-supervised Clustering. We apply the model to the Booking.com data related to the Sardinian hotels. The first results highlight the presence of different clusters non-overlapped in terms of the distribution of the overall sentiment.