Abstract
Nowadays, with the continuous development of the Internet, public opinion analysis has become an indispensable means for governments and companies to grasp public opinion trends and respond promptly to emergencies. Finding out a topic’s relevant comments is more conducive to providing analysis foundation. Twitter, a popular social media website, permits users to post their viewpoints about an event. An event’s relevant comments could be obtained through twitter search using the event’s key phrases. However, twitter search utilizes the full-match mode, which the search results contain a large number of irrelevant comments for it doesn’t use a correlation filter. In this paper, we proposed a framework for identifying twitter relevance comments (ITRC). The framework treats ITRC as a text matching task and matches one comment with all news of an event to distinguish whether the comment is a relevance comment in twitter. Before the matching module, we adopted the Rake algorithm to extract key phrases from the event’s news and then the key phrases were used for twitter search to construct twitter relevant comments dataset. Based on this dataset, the effectiveness of different text matching methods was examined. Through the in-event and cross-event experiments, we used the MV-LSTM with the best overall performance as our matching module. Moreover, we also mixed data from different events to conduct the experiments. The experimental results demonstrated the effectiveness of the mixed data strategy.
D. Peng and N. Lin are co-first authors of the article.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61572145), the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (No. 2017KZDXM031) and National Social Science Foundation of China (No. 17CTQ045). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.
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Peng, D., Lin, N., Lin, X., Yan, X., Jiang, S. (2020). A Framework for Identifying Event’s Relevance Comments in Twitter. In: Dou, Z., Miao, Q., Lu, W., Mao, J., Jia, G. (eds) Information Retrieval. CCIR 2020. Lecture Notes in Computer Science(), vol 12285. Springer, Cham. https://doi.org/10.1007/978-3-030-56725-5_4
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DOI: https://doi.org/10.1007/978-3-030-56725-5_4
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