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A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

  • María Hernández-Rubio
  • Iván Cantador
  • Alejandro Bellogín
Article

Abstract

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based top-N recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexicons.

Keywords

Recommender systems Aspect-based recommendation Sentiment analysis Opinion mining Aspect extraction User reviews 

Notes

Acknowledgements

This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P). The authors thank the reviewers for their thoughtful comments and suggestions.

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Authors and Affiliations

  1. 1.BBVA Data and AnalyticsMadridSpain
  2. 2.Departamento de Ingeniería InformáticaUniversidad Autónoma de MadridMadridSpain

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