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.
Highlights
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
Because there is no work in Bangla for the aspect-based sentiment analysis (ABSA) task, we have introduced ABSA by extracting aspect categories from Bangla texts in order to evaluate our datasets
They published their dataset with four fields being contained for each review, that is, with the aspect term occurring in the sentences, the aspect term’s polarity, the aspect category, and the aspect category’s polarity. They provided a laptop-review dataset and manually annotated with similar entities as for the restaurant dataset. These are the benchmark datasets that [8,9,10,11] researches have used for performing the ABSA task
Summary
People trust human opinion more so than traditional advertising. For example, consumers are used to seeking advice and recommendation from others before making decisions regarding important purchases. The restaurant review dataset, provided by Ganu et al [7], was used to improve rating predictions Their annotations included six aspect categories and overall sentence polarities. They published their dataset with four fields being contained for each review, that is, with the aspect term occurring in the sentences, the aspect term’s polarity, the aspect category, and the aspect category’s polarity They provided a laptop-review dataset and manually annotated with similar entities as for the restaurant dataset. These are the benchmark datasets that [8,9,10,11] researches have used for performing the ABSA task.
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