Abstract
Microblogging sentiment analysis aims at exploring people’s opinion on social networks such as Twitter and Weibo. Existing work mainly focus on the English corpus based on Distant Supervision, which ignores the noise data in corpus and internationalization. The field of Weibo sentiment analysis lacks a large-scale and complete corpus for application and evaluation. In this work, we formulate the problem of corpus construction into an Information Retrieval problem and construct a Weibo sentiment analysis corpus called Senti-weibo. We also release a weibo pre-processing toolkit in order to unify the pre-processing rules of Weibo text. Eventually, we apply these works to implement a real-time Weibo sentiment analysis platform: S2AP, which serves to analyze and track the sequential sentiment of Weibo topics.
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Wan, S., Li, B., Zhang, A., Wang, W., Guan, D. (2020). S2AP: Sequential Senti-Weibo Analysis Platform. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_49
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DOI: https://doi.org/10.1007/978-3-030-59419-0_49
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