Deep attention-based model for helpfulness prediction of healthcare online reviews

Consoli S.

Membro del Collaboration Group

;Dessi D.

Writing – Original Draft Preparation

;Fenu G.

Membro del Collaboration Group

;Marras M.

Writing – Original Draft Preparation

2020-01-01

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Abstract

With tons of healthcare reviews being collected online, finding helpful opinions among this collective intelligence is becoming harder. Existing literature in this domain usually tackled helpfulness prediction with machine-learning models optimized for binary classification. While they can filter out a subset of reviews, users might be still overwhelmed if the number of reviews marked as helpful is high. In this paper, we design a new neural model optimized for predicting a continuous score that can be used to rank reviews based on their helpfulness. Given embedding representations of words in a review, the proposed model processes them through recurrent and attention-based layers to solve a helpfulness prediction task, modeled as a regression. Experiments on a real-world healthcare dataset show that the proposed model optimized for regression leads to accurate helpfulness prediction and better helpfulness-based rankings than models optimized for binary classification.