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Analyzing Textual Information at Scale

In: Information for Efficient Decision Making Big Data, Blockchain and Relevance

Author

Listed:
  • Lin William Cong
  • Tengyuan Liang
  • Baozhong Yang
  • Xiao Zhang

Abstract

We provide an overview on the recent advances in textual analysis for social sciences. Count-based economic model, structured statistical tool, and plain-vanilla machine learning apparatus each have their own merits and limitations. To take a data-driven approach to capture complex linguistic structures while ensuring computational scalability and economic interpretability, a general framework for analyzing large-scale text-based data is needed. We discuss the recent attempts combining the strengths of neural network language models, such as word embedding, and generative statistical modeling, such as topic modeling. We also describe typical sources of texts and the applications of these methodologies to issues in finance and economics and discuss promising future directions.

Suggested Citation

  • Lin William Cong & Tengyuan Liang & Baozhong Yang & Xiao Zhang, 2020. "Analyzing Textual Information at Scale," World Scientific Book Chapters, in: Kashi R Balachandran (ed.), Information for Efficient Decision Making Big Data, Blockchain and Relevance, chapter 10, pages 239-271, World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789811220470_0010
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    More about this item

    Keywords

    Accounting; Decision Making; Economic Information; Stock Trading; Acquisitions; Mergers; Financing Reporting; Reliable Information; Company Decisions;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • A1 - General Economics and Teaching - - General Economics

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