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    • Comquest: Large Scale User Comment Crawling and Integration

    Zhijia Chen,Lihong He,Arjun Mukherjee,Eduard Dragut

    COMPANION OF THE 2024 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, SIGMOD-COMPANION 2024（2024）

    Temple Univ

    Cited 2|Views8
    Abstract
    User-generated content like comments are valuable sources for various downstream applications. However, access to user comments data is often limited to specific platforms or outlets, which imposes a great limitation on the available data, and may not provide a representative sample of opinions from a diverse population on a particular event. This paper presents a comment crawling system that leverages the Web API of popular third-party commenting systems to collect comments from a large number of websites integrated with the commenting systems. Given a target page, the crawling system utilizes a deep learning model to extract API parameters and send HTTP requests to the API to retrieve comments. The system, Comquest, that we propose to demo is news-oriented and crawls comments regarding specific news topics/stories. Comquest can work with any website that allows commenting. Comquest provides a useful tool for collecting comments that represent a wider range of opinions, stances, and sentiments from websites on a global scale.
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    Key words
    comments,crawling,Web API
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
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