DemandMiner: Extraction and Visualization of Product Demands from Open Web
Authors: 
Tirthankar Dasgupta, Lipika Dey, Abir Naskar, Rupsa Saha, Ishan Verma, Ramakrishnan SrinivasanAuthors Info & Claims
Tirthankar Dasgupta, Lipika Dey, Abir Naskar, Rupsa Saha, Ishan Verma, Ramakrishnan SrinivasanAuthors Info & ClaimsPages 136 - 143
Abstract
In this paper, we present the DemandMiner, an online tool that leverages Natural Language Processing and Machine Learning techniques to extract demand related to different industries from large volume of News articles. We also propose techniques to enrich the information components extracted from a document by associating them with their sense oriented entities. The enriched components are then analyzed for generating informed insights to generate contextual reports that can convey greater sense to the decision-makers, analysts and knowledge workers than simple display of events. An interactive visualization system is also provided for searching and querying the underlying collection of data and the derived insights.
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- DemandMiner: Extraction and Visualization of Product Demands from Open Web
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Published In
September 2017
202 pages
ISBN:9781450352963
DOI:10.1145/3132218
Copyright © 2017 ACM.
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In-Cooperation
- St. Pölten University: St. Pölten University of Applied Sciences, Austria
- Wolters Kluwer: Wolters Kluwer, Germany
- Vrije Universeit Amsterdam: Vrije Universeit Amsterdam
- Semantic Web Company: Semantic Web Company
- Uinv. Leipzig: Universität Leipzig
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Association for Computing Machinery
New York, NY, United States
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Published: 11 September 2017
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Semantics2017
Semantics2017: Semantics 2017 - 13th International Conference on Semantic Systems
September 11 - 14, 2017
Amsterdam, Netherlands
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Overall Acceptance Rate 40 of 182 submissions, 22%
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