Abstract
Sentiment analysis on social media represented by Weibo is one of the hotspot research problems in NLP. A comprehensive and systematic fine-grained annotated corpus plays a significance role. In this paper, considering the characteristics of Weibo, we focus on the constitution of a fine-grained, hierarchical opinion annotated corpus and design a set of labelling specification. We manually annotate the opinion sentences with a part of ones containing hidden opinion which can be useful for implicit sentiment analysis. Then a fine-grained aspect extraction, namely opinion triples like <object, attribute, polarity> is finished for aspect-level sentiment research. Moreover, we establish an evaluation method for the task of fine-grained aspect extraction which has been applied in evaluation for years. The corpus was used in the task of COAE2015, and it will be a useful resource for the related research on social media sentiment analysis.
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Notes
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The coverage match is equivalent to fully match when the coverage is 1.
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Acknowledgement
The authors would like to thank all the students’ hard work who participate the corpus’s labelling including Zhao Celi, Zhang Jin, Xu Chaoyi, Guo Xiaomin, Zhang Jun, Li Min, Qiao Pei, Mu Wanqing, Wang Jia, Wang Jie and Lv Ying. Also thank all anonymous reviewers for their valuable comments and suggestions which have significantly improved the quality and presentation of this paper. This work was supported by the National High-Tech Research and Development Program (863 Program) (2015AA011808); the National Natural Science Foundation of China (61432011, 61573231, 61175067, 61272095, U1435212); the Shanxi Province Returned Overseas Research Project (2013-014); the Shanxi Province Science and Technology Basic Condition Platform Construction (2015091001-0102).
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Jian, L., Yang, L., Suge, W. (2016). The Constitution of a Fine-Grained Opinion Annotated Corpus on Weibo. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_20
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