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Browsing by Author "Du, Qianzhou"

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    Extracting the Wisdom of Crowds From Crowdsourcing Platforms
    Du, Qianzhou (Virginia Tech, 2019-08-02)
    Enabled by the wave of online crowdsourcing activities, extracting the Wisdom of Crowds (WoC) has become an emerging research area, one that is used to aggregate judgments, opinions, or predictions from a large group of individuals for improved decision making. However, existing literature mostly focuses on eliciting the wisdom of crowds in an offline context—without tapping into the vast amount of data available on online crowdsourcing platforms. To extract WoC from participants on online platforms, there exist at least three challenges, including social influence, suboptimal aggregation strategies, and data sparsity. This dissertation aims to answer the research question of how to effectively extract WoC from crowdsourcing platforms for the purpose of making better decisions. In the first study, I designed a new opinions aggregation method, Social Crowd IQ (SCIQ), using a time-based decay function to eliminate the impact of social influence on crowd performance. In the second study, I proposed a statistical learning method, CrowdBoosting, instead of a heuristic-based method, to improve the quality of crowd wisdom. In the third study, I designed a new method, Collective Persuasibility, to solve the challenge of data sparsity in a crowdfunding platform by inferring the backers' preferences and persuasibility. My work shows that people can obtain business benefits from crowd wisdom, and it provides several effective methods to extract wisdom from online crowdsourcing platforms, such as StockTwits, Good Judgment Open, and Kickstarter.
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    Named Entity Recognition for IDEAL
    Du, Qianzhou; Zhang, Xuan (2015-05-10)
    The term “Named Entity”, which was first introduced by Grishman and Sundheim, is widely used in Natural Language Processing (NLP). The researchers were focusing on the information extraction task, that is extracting structured information of company activities and defense related activities from unstructured text, such as newspaper articles. The essential part of “Named Entity” is to recognize information elements, such as location, person, organization, time, date, money, percent expression, etc. To identify these entities from unstructured text, some researchers called this sub-task of information extraction as “Named Entity Recognition” (NER). Now, NER technology has become mature and there are good tools to implement this task, such as the Stanford Named Entity Recognizer (SNER), Illinois Named Entity Tagger (INET), Alias-i LingPipe (LIPI), and OpenCalasi (OCWS). Each of these has some advantages and is designed for some special data. In this term project, our final goal is to build a NER module for the IDEAL project based on a particular NER tool, such as SNER, to apply NER to the Twitter and web pages data sets. This project report presents our work towards this goal, including literature review, requirements, algorithm, development plan, system architecture, implementation, user manual, and development manual. Further, results are given with regard to multiple collections, along with discussion and plans for the future.