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E-reputation monitoring on Twitter with active learning automatic annotation

Abstract : Opinion and trend mining on micro blogs like twitter recently attracted research interest in several fields including Information Retrieval and Machine Learning. This paper is intended to develop a so-called active learning for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of entities : such as politicians, celebrities, companies or brands. Our main contribution is the methodology followed to build and provide an original annotated French data-set expressing opinion on two French politicians over time. Since the performance of natural language processing tasks are limited by the amount and quality of data available to them, one promising alternative for some tasks is the propagation of pseudo-expert annotations. The paper is focused on key issues about active learning while building a large annotated data set, from noise introduced by humans annotators, abundance of data and the label distribution across data and entities.
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Submitted on : Tuesday, June 10, 2014 - 1:02:22 PM
Last modification on : Wednesday, April 22, 2020 - 2:50:09 PM
Long-term archiving on: : Wednesday, September 10, 2014 - 11:11:15 AM


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  • HAL Id : hal-01002818, version 1



Jean-Valère Cossu, Marc El Bèze, Juan-Manuel Torres-Moreno, Eric Sanjuan. E-reputation monitoring on Twitter with active learning automatic annotation. 2014. ⟨hal-01002818⟩



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