VLSP SHARED TASK: SENTIMENT ANALYSIS
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DOI:
https://doi.org/10.15625/1813-9663/34/4/13160Keywords:
aspect based sentiment analysis, evaluation, opinion mining, sentiment analysis, shared task, Vietnamese, VLSP workshopAbstract
Sentiment analysis is a natural language processing (NLP) task of identifying or
extracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encourage
the development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.
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References
P. et al., "Semeval-2016 task 5: Aspect based sentiment analysis." in In Proceedings of SemEval-2016, 2016, pp. 19-30.
N. Hy, L. Tung, L. Viet-Thang, and D. Dien, "A simple supervised learning approach to sentiment classication at vlsp 2016," in in The Fourth International Workshop on Vietnamese Language and Speech Processing (VLSP 2016), 2016. [Online]. Available:
B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.
P. Nakov, A. Ritter, S. Rosenthal, F. Sebastiani, and V. Stoyanov, "Semeval-2016 task 4: Sentiment analysis in twitter," in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, US, 2016, pp. 1-18.
T. A. Nguyen and P. Q. N. Minh, "Using multilayer perceptron for aspect-based sentiment analysis at vlsp-2018 sa task," in In Proceedings of the Fifth International workshop on Vietnamese Language and Speech Processing (VLSP 2018), 2018. [Online]. Available:
P. M. Q. Nhat and T. T. Tran, "A lightweight ensemble method for sentiment classication task," in in The Fourth International Workshop on Vietnamese Language and Speech Processing (VLSP 2016), 2016. [Online]. Available: http://vlsp.org.vn/archives
T. P. Quynh-Trang, N. Xuan-Truong, T. Van-Hien, N. Thi-Cham, and T. Mai-Vu, "Dsktlab: Vietnamese sentiment analysis for product reviews," in The Fourth International Workshop on Vietnamese Language and
Speech Processing (VLSP 2016), 2016. [Online]. Available: http://vlsp.org.vn/archives
T. T. Tran, X. Ho, and N. T. Nguyen, "A multi-layer neural network based system for vietnamese sentiment analysis at the vlsp 2016 evaluation campaign," in in The Fourth International Workshop on Vietnamese Language and Speech Processing (VLSP 2016), 2016.
[Online]. Available: http://vlsp.org.vn/archives
T. D. Van, K. V. Nguyen, and N. L.-T. Nguyen., "Nlp@uit at vlsp 2018: A supervised method for aspect based sentiment analysis," in In Proceedings of the Fifth International workshop on Vietnamese Language and Speech Processing (VLSP 2018), 2018. [Online]. Available:
N. V. Vi, H. V. Minh, and N. T. Tam, "Sentiment analysis for vietnamese using support vector machines with application to facebook," in in The Fourth International Workshop on Vietnamese Language and Speech Processing (VLSP 2016), 2016. [Online]. Available:
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