Profiling topics on the Web for knowledge discovery

Srinivasan, Padmini; Sehgal, Aditya Kumar

doi:10.17077/etd.yvanio01

Cited by 2 publications

(2 citation statements)

References 84 publications

(105 reference statements)

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“…(4) is constrained to the [0.5, 1] range to boost the score for features that occur less frequently, relative to features that are mentioned more frequently. This augment would consequently prevent the FF À IPF score to converge to zero (hence becoming nondiscriminative) for features that are less common [54]. The set of extracted latent features will be used to identify customers who possess and express innovative ideas, whom are referred to as lead users.…”

Section: Objectivementioning

confidence: 99%

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

Tuarob

Tucker

2015

Journal of Mechanical Design

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Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

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Section: Objectivementioning

confidence: 99%

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

Tuarob

Tucker

2015

Journal of Mechanical Design

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“…With the help of semantic web mining technologies (Gerd, Andreas, and Bettina 2006), we could apply the profiling application into web resources. Sehgal (2007) proposed a profile-based approach to explore the social networks of US senators generated from web data. The social networks were then compared with networks generated from voting data.…”

Section: Introductionmentioning

confidence: 99%

Profiling science and innovation policy by object-based computing

Zhang

Liu

Zou

et al. 2014

Technology Analysis & Strategic Management

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Named entities, such as key initiatives, research programmes, scientific strategies and policies, are research objects or objects that are embedded in many web pages of science and innovation institutes. These objects provide important information that can be extracted intelligently from those pages. This paper brings forward an object-based computing method by using objects and their semantic information for profiling science and innovation policies. After extracting the objects and the relationships between them, we proposed an object grid to represent web pages. Objects were transferred into machine-readable knowledge units with rich semantic information. By using computational objects, we judged the intelligence value of web resources and classified policies into more detailed categories, such as strategic plan, research and development, and budget. To test the effectiveness of profiling science and innovation policies by using the object-based computing method, this paper conducted a research profiling experiment system.

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Profiling topics on the Web for knowledge discovery

Cited by 2 publications

References 84 publications

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

Profiling science and innovation policy by object-based computing

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