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Web Text-based Network Industry Classifications: Preliminary Results

Published: 14 May 2017 Publication History

Abstract

Studies of market structure and product market competition are important in many disciplines, such as economics, finance, accounting and management. Reliable data for such studies is easily available for public firms (e.g., 10-K filings), but no reliable data exists for private firms. In this work we propose to mine the Internet Archive Wayback Machine, a digital archive of the World Wide Web, to build a database of 300,000 companies to support analyses of market structure, product market competition, and innovation. The goal of the WTNIC project is to download pages from the archive to build a profile for each company, and to use machine learning techniques to define similarity between companies based on similarity of their product and service offerings. This paper describes the challenges that must be overcome, our approach to overcome these challenges, and some preliminary results.

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Clinton Gormley and Zachary Tong. 2015. Elasticsearch: The Definitive Guide. O'Reilly Media, Inc.".
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Gerard Hoberg and Gordon Phillips. 2016. Text-based network industries and endogenous product differentiation. Journal of Political Economy 124, 5 (2016), 1423--1465.
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Stephen Robertson, Hugo Zaragoza, and others. 2009. The probabilistic relevance framework: BM25 and beyond. Foundations and Trends® in Information Retrieval 3, 4 (2009), 333--389.

Cited By

View all
  • (2021)Technology Intelligence Map: Finance Machine LearningRoadmapping Future10.1007/978-3-030-50502-8_10(337-356)Online publication date: 17-Mar-2021
  • (2018)Feature Selection Methods For Understanding Business Competitor RelationshipsProceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets10.1145/3220547.3220550(1-6)Online publication date: 15-Jun-2018
  • (2018)Automated Industry Classification with Deep Learning2018 IEEE 12th International Conference on Semantic Computing (ICSC)10.1109/ICSC.2018.00018(64-70)Online publication date: Jan-2018
  • Show More Cited By

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cover image ACM Conferences
DSMM'17: Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets
May 2017
58 pages
ISBN:9781450350310
DOI:10.1145/3077240
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2017

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Author Tags

  1. Document classification
  2. TNIC
  3. company similarity
  4. competitive landscape
  5. industry classifications

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SIGMOD/PODS'17
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Overall Acceptance Rate 32 of 64 submissions, 50%

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Cited By

View all
  • (2021)Technology Intelligence Map: Finance Machine LearningRoadmapping Future10.1007/978-3-030-50502-8_10(337-356)Online publication date: 17-Mar-2021
  • (2018)Feature Selection Methods For Understanding Business Competitor RelationshipsProceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets10.1145/3220547.3220550(1-6)Online publication date: 15-Jun-2018
  • (2018)Automated Industry Classification with Deep Learning2018 IEEE 12th International Conference on Semantic Computing (ICSC)10.1109/ICSC.2018.00018(64-70)Online publication date: Jan-2018
  • (2017)Automated industry classification with deep learning2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8257920(122-129)Online publication date: Dec-2017

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