Focused Concept Miner (FCM): Interpretable Deep Learning for Text Exploration

42 Pages Posted: 8 Jul 2019 Last revised: 1 Oct 2019

See all articles by Dokyun (DK) Lee

Dokyun (DK) Lee

Carnegie Mellon University - David A. Tepper School of Business

Emaad Manzoor

Carnegie Mellon University, Students

Zhaoqi Cheng

Carnegie Mellon University - David A. Tepper School of Business

Date Written: November 20, 2018

Abstract

We introduce the Focused Concept Miner (FCM), an interpretable deep learning text mining algorithm to (1) automatically extract interpretable high-level concepts from text data, (2) focus the mined concepts to explain user-specified business outcomes, such as conversion (linked to read-reviews) or crowdfunding success (linked to project descriptions), and (3) quantify the correlational relative importance of each concept for business outcomes against one another and to other explanatory variables. Compared to 4 interpretable and 4 prediction-focused baselines that partially achieve FCM's goals, FCM attains higher interpretability, as measured by a variety of metrics (e.g., automated, human-judged), while achieving competitive predictive performance even when compared to prediction-focused blackbox algorithms.

The relative importance of discovered concepts provides managers and researchers with easy ways to gauge potential impact and to augment hypotheses development. We present FCM as a complimentary technique to explore and understand unstructured textual data before applying standard causal inference techniques.

Applications can be found in any setting with text and structured data tied to a business outcome. We evaluate FCM’s performance on 3 datasets in e-commerce, crowdfunding, and 20-NewsGroup. Plus, 2 experiments investigate the accuracy-interpretability relationship to provide empirical observations for interpretable machine learning literature along with the impact of focusing variables on extracted concepts. The paper concludes with ideas for future development, potential applications, and managerial implications.

Keywords: Interpretable Machine Learning, Deep Learning, Text Mining, Automatic Concept Extraction, Coherence, Transparent Algorithm, Managerial Exploratory Tool, XAI

JEL Classification: C38, C39, M31, M39

Suggested Citation

Lee, Dokyun (DK) and Manzoor, Emaad and Cheng, Zhaoqi, Focused Concept Miner (FCM): Interpretable Deep Learning for Text Exploration (November 20, 2018). Available at SSRN: https://ssrn.com/abstract=3304756 or http://dx.doi.org/10.2139/ssrn.3304756

Dokyun (DK) Lee (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Emaad Manzoor

Carnegie Mellon University, Students ( email )

Pittsburgh, PA
United States

Zhaoqi Cheng

Carnegie Mellon University - David A. Tepper School of Business ( email )

Pittsburgh, PA
United States

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