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A complete framework for aspect-level and sentence-level sentiment analysis

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Abstract

Aspect-Based Sentiment Analysis (ABSA) and Sentence-Based Sentiment Analysis (SBSA) stand for two highly coupled study fields. Basically, the features required at the sentence level influence and depend on the aspect level and vice versa. Nevertheless, a few approaches have considered the correlation between these two tasks. This research work is interested in both aspect and sentence levels. It starts with the ABSA which is in turn divided into two strongly coupled tasks, namely the aspect extraction and the aspect sentiment classification. Indeed, integrating highly coupled tasks into an integrated model can lead to more significant performance improvement rather than in the case of separate models, which is also confirmed through the proposed ABSA model. The latter represents a unified-trained model based on deep learning techniques for extracting the aspects along with their sentiment polarities. Later on, the emphasis would be put on SBSA, which is a complex study, especially with the existence of opinions that include several aspects with opposing polarities. From this perspective, a combination of deep learning and fuzzy logic techniques was elaborated to address this issue. The hybrid model achieved satisfactory performance compared to the Bert model.

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Acknowledgements

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

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  1. REGIM-Lab.: Research Groups in Intelligent Machines, National School of Engineers (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Rim Chiha & Mounir Ben Ayed

  2. Faculty of Science of Sfax, University of Sfax, BP 802, 3038, Sfax, Tunisia

    Mounir Ben Ayed

  3. University of Côte d’Azur, I3S, CNRS, Nice, France

    Célia da Costa Pereira

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Chiha, R., Ayed, M.B. & Pereira, C.d.C. A complete framework for aspect-level and sentence-level sentiment analysis. Appl Intell 52, 17845–17863 (2022). https://doi.org/10.1007/s10489-022-03279-9

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