An interpersonal sentiment quantification method applied to work relationship prediction

Miyuki Imada, Kei Hirose, Manabu Yoshida, Sun Yong Kim, Naoya Toyozumi, Guillaume Lopez, Yutaka Kano

Research output: Contribution to journalReview article

Abstract

For a business to be successful, it is important for people in the business to consider how other people feel, that is, to consider interpersonal sentiment. Our research goal is to quantitatively predict the strength of interpersonal sentiment by analyzing a small amount of data on office employees, for example, their gender or age group, and data on events such as giving positive feedback on work done and sexual or power harassment without directly asking someone about their change in sentiment. In this article, we propose an interpersonal-sentiment-changing model for this quantification and propose two new analysis methods for developing prediction formulas. These methods can be used even if 90% of data is missing and in environments in which it is difficult to gather data in a comparatively short time. We also implement two visualization systems to predict how interpersonal sentiment changes for each event based on actual office data.

Original languageEnglish
JournalNTT Technical Review
Volume15
Issue number3
Publication statusPublished - Mar 1 2017

Fingerprint

Industry
Visualization
Personnel
Feedback

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Imada, M., Hirose, K., Yoshida, M., Kim, S. Y., Toyozumi, N., Lopez, G., & Kano, Y. (2017). An interpersonal sentiment quantification method applied to work relationship prediction. NTT Technical Review, 15(3).

An interpersonal sentiment quantification method applied to work relationship prediction. / Imada, Miyuki; Hirose, Kei; Yoshida, Manabu; Kim, Sun Yong; Toyozumi, Naoya; Lopez, Guillaume; Kano, Yutaka.

In: NTT Technical Review, Vol. 15, No. 3, 01.03.2017.

Research output: Contribution to journalReview article

Imada, M, Hirose, K, Yoshida, M, Kim, SY, Toyozumi, N, Lopez, G & Kano, Y 2017, 'An interpersonal sentiment quantification method applied to work relationship prediction', NTT Technical Review, vol. 15, no. 3.
Imada, Miyuki ; Hirose, Kei ; Yoshida, Manabu ; Kim, Sun Yong ; Toyozumi, Naoya ; Lopez, Guillaume ; Kano, Yutaka. / An interpersonal sentiment quantification method applied to work relationship prediction. In: NTT Technical Review. 2017 ; Vol. 15, No. 3.
@article{27d748ca61c241b6ab57ae713e3bc9b2,
title = "An interpersonal sentiment quantification method applied to work relationship prediction",
abstract = "For a business to be successful, it is important for people in the business to consider how other people feel, that is, to consider interpersonal sentiment. Our research goal is to quantitatively predict the strength of interpersonal sentiment by analyzing a small amount of data on office employees, for example, their gender or age group, and data on events such as giving positive feedback on work done and sexual or power harassment without directly asking someone about their change in sentiment. In this article, we propose an interpersonal-sentiment-changing model for this quantification and propose two new analysis methods for developing prediction formulas. These methods can be used even if 90{\%} of data is missing and in environments in which it is difficult to gather data in a comparatively short time. We also implement two visualization systems to predict how interpersonal sentiment changes for each event based on actual office data.",
author = "Miyuki Imada and Kei Hirose and Manabu Yoshida and Kim, {Sun Yong} and Naoya Toyozumi and Guillaume Lopez and Yutaka Kano",
year = "2017",
month = "3",
day = "1",
language = "English",
volume = "15",
journal = "NTT Technical Review",
issn = "1348-3447",
publisher = "Nippon Telegraph and Telephone Corp.",
number = "3",

}

TY - JOUR

T1 - An interpersonal sentiment quantification method applied to work relationship prediction

AU - Imada, Miyuki

AU - Hirose, Kei

AU - Yoshida, Manabu

AU - Kim, Sun Yong

AU - Toyozumi, Naoya

AU - Lopez, Guillaume

AU - Kano, Yutaka

PY - 2017/3/1

Y1 - 2017/3/1

N2 - For a business to be successful, it is important for people in the business to consider how other people feel, that is, to consider interpersonal sentiment. Our research goal is to quantitatively predict the strength of interpersonal sentiment by analyzing a small amount of data on office employees, for example, their gender or age group, and data on events such as giving positive feedback on work done and sexual or power harassment without directly asking someone about their change in sentiment. In this article, we propose an interpersonal-sentiment-changing model for this quantification and propose two new analysis methods for developing prediction formulas. These methods can be used even if 90% of data is missing and in environments in which it is difficult to gather data in a comparatively short time. We also implement two visualization systems to predict how interpersonal sentiment changes for each event based on actual office data.

AB - For a business to be successful, it is important for people in the business to consider how other people feel, that is, to consider interpersonal sentiment. Our research goal is to quantitatively predict the strength of interpersonal sentiment by analyzing a small amount of data on office employees, for example, their gender or age group, and data on events such as giving positive feedback on work done and sexual or power harassment without directly asking someone about their change in sentiment. In this article, we propose an interpersonal-sentiment-changing model for this quantification and propose two new analysis methods for developing prediction formulas. These methods can be used even if 90% of data is missing and in environments in which it is difficult to gather data in a comparatively short time. We also implement two visualization systems to predict how interpersonal sentiment changes for each event based on actual office data.

UR - http://www.scopus.com/inward/record.url?scp=85016025754&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016025754&partnerID=8YFLogxK

M3 - Review article

AN - SCOPUS:85016025754

VL - 15

JO - NTT Technical Review

JF - NTT Technical Review

SN - 1348-3447

IS - 3

ER -