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Survey on aspect detection for aspect-based sentiment analysis

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Abstract

Sentiment analysis is an important tool to automatically understand the user-generated content on the Web. The most fine-grained sentiment analysis is concerned with the extraction and sentiment classification of aspects and has been extensively studied in recent years. In this work, we provide an overview of the first step in aspect-based sentiment analysis that assumes the extraction of opinion targets or aspects. We define a taxonomy for the extraction of aspects and present the most relevant works accordingly, with a focus on the most recent state-of-the-art methods. The three main classes we use to classify the methods designed for the detection of aspects are pattern-based, machine learning, and deep learning methods. Despite their differences, only a small number of works belong to a unique class of methods. All the introduced methods are ranked in terms of effectiveness. In the end, we highlight the main ideas that have led the research on this topic. Regarding future work, we deemed that the most promising research directions are the domain flexibility and the end-to-end approaches.

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Notes

  1. Since the method extracts both implicit and explicit aspects simultaneously, the results are not presented in Table 1.

  2. A more recent version (SenticNet 6) is already available (Cambria et al. 2020).

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  1. Department of Informatics and Economic Cybernetics, Bucharest University of Economic Studies, Piata Romana 6, 010374, Bucharest, Romania

    Maria Mihaela Truşcǎ

  2. Erasmus School of Economics, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, The Netherlands

    Flavius Frasincar

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Truşcǎ, M.M., Frasincar, F. Survey on aspect detection for aspect-based sentiment analysis. Artif Intell Rev 56, 3797–3846 (2023). https://doi.org/10.1007/s10462-022-10252-y

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