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Classification and Summarization for Informative Tweets

机译：信息性推文的分类和汇总

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Microblogging websites like twitter, facebook, etc. has become a substantive platform for the people to publicize their feelings, requirements, etc. It allows users to post short messages for their online audience. These messages are the fusion of blogging and minute messaging, consisting of images, videos, or voice notes. We have primarily focused on information provided by microblogging sites for achieving real-time informational data. Microblogging websites are widely used around the globe by people for portraying what has been happening around their normal living. So, data through these sites eventually helps us getting non-manipulated data directly from the user. In this paper, a disaster dataset (Fani Cyclone dataset) is considered, which consists of the tweets related to a Cyclone named "Fani". The tweets are pre-processed and then classified into two categories - informative and non-informative. We have been able to achieve a classification accuracy of 74:268% when pre-processed data is being considered. As we are dealing with disaster dataset, so in the end, we have summarized the informative tweets for the concerned authorities, which would help them to have an overview of the data.
机译：微博网站，例如twitter，facebook等，已经成为人们宣传自己的感受，要求等的实质性平台。它允许用户向其在线受众发布短消息。这些消息是博客和分钟消息的结合，由图像，视频或语音注释组成。我们主要关注微博站点提供的信息，以实现实时信息数据。人们在全球范围内广泛使用微博网站来描绘其正常生活中正在发生的事情。因此，通过这些站点获得的数据最终将帮助我们直接从用户那里获得未经处理的数据。在本文中，考虑了一个灾难数据集（Fani Cyclone数据集），该数据集由与名为“ Fani”的旋风有关的推文组成。这些推文经过预处理，然后分为两类-信息性和非信息性。当考虑到预处理数据时，我们已经能够实现74：268％的分类精度。由于我们正在处理灾难数据集，因此最后，我们总结了有关当局的信息性推文，这将有助于他们对数据进行概述。

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