Abstract
In this paper, we present an overview of the NLPCC 2020 shared task on Multi-Aspect-based Multi-Sentiment Analysis (MAMS). The evaluation consists of two sub-tasks: (1) aspect term sentiment analysis (ATSA) and (2) aspect category sentiment analysis (ACSA). We manually annotated a large-scale restaurant reviews corpus for MAMS, in which each sentence contains at least two different aspects with different sentiment polarities. Thus, the provided MAMS dataset is more challenging than the existing aspect-based sentiment analysis (ABSA) datasets. MAMS attracted a total of 50 teams to participate in the evaluation task. We believe that MAMS will push forward the research in the field of aspect-based sentiment analysis.
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Chen, L., Xu, R., Yang, M. (2020). Overview of the NLPCC 2020 Shared Task: Multi-Aspect-Based Multi-Sentiment Analysis (MAMS). In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_48
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DOI: https://doi.org/10.1007/978-3-030-60457-8_48
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