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Datasets for Aspect-Based Sentiment Analysis in Bangla and Its Baseline Evaluation

Author

Listed:
  • Md. Atikur Rahman

    (Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh)

  • Emon Kumar Dey

    (Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh)

Abstract

With the extensive growth of user interactions through prominent advances of the Web, sentiment analysis has obtained more focus from an academic and a commercial point of view. Recently, sentiment analysis in the Bangla language is progressively being considered as an important task, for which previous approaches have attempted to detect the overall polarity of a Bangla document. To the best of our knowledge, there is no research on the aspect-based sentiment analysis (ABSA) of Bangla text. This can be described as being due to the lack of available datasets for ABSA. In this paper, we provide two publicly available datasets to perform the ABSA task in Bangla. One of the datasets consists of human-annotated user comments on cricket, and the other dataset consists of customer reviews of restaurants. We also describe a baseline approach for the subtask of aspect category extraction to evaluate our datasets.

Suggested Citation

  • Md. Atikur Rahman & Emon Kumar Dey, 2018. "Datasets for Aspect-Based Sentiment Analysis in Bangla and Its Baseline Evaluation," Data, MDPI, vol. 3(2), pages 1-10, May.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:2:p:15-:d:144525
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    Cited by:

    1. Matteo Bodini, 2019. "Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders," Data, MDPI, vol. 4(3), pages 1-20, August.

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