摘要: In recent years, Text Mining has seen a tremendous spurt of growth as data scientists focus their attention on analyzing unstructured data. The main drivers for this have been big well complex applications where the information in text is often combined with other kinds building predictive models. These require highly efficient and scalable algorithms to meet overall performance demands. context, six directions are identified research mining heading: Deep Learning, Topic Models, Graphical Modeling, Summarization, Sentiment Analysis, Learning from Unlabeled Text. Each direction its own motivations goals. There some overlap concepts because common themes prediction. models involved typically ones that involve meta-information or tags could be added text. can then used processing tasks such extraction. While boundary between fields Natural Language Processing becoming increasingly blurry, importance various involving means there still substantial potential within traditional sub-fields mining. data-centric also likely influence future Processing, especially resource-poor languages multilingual texts. WIREs Data Knowl Discov 2015, 5:155-164. doi: 10.1002/widm.1154
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