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WS4ABSA: An NMF-Based Weakly-Supervised Approach for Aspect-Based Sentiment Analysis with Application to Online Reviews

  • Alberto PurpuraEmail author
  • Chiara MasieroEmail author
  • Gian Antonio SustoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)

Abstract

The goal of Aspect-Based Sentiment Analysis is to identify opinions regarding specific targets and the corresponding sentiment polarity in a document. The proposed approach is designed for real-world scenarios, where the amount of available information and annotated data is often too limited to train supervised models. We focus on the two core tasks of Aspect-Based Sentiment Analysis: aspect and sentiment polarity classification. The first task – which consists in the identification of the opinion targets in a document – is tackled by means of a weakly-supervised technique based on Non-negative Matrix Factorization. This strategy allows users to easily embed some a priori domain knowledge by means of short seed terms lists. Experimental results on publicly available data sets related to online reviews suggest that the proposed approach is very flexible and can be easily adapted to different languages and domains.

Keywords

Aspect-based sentiment analysis Non-negative matrix factorization Text mining Weakly-supervised learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of PadovaPadovaItaly

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