Abstract
Purpose: User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations. Design/methodology/approach: The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data. Findings: After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation. Practical implications: For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN. Originality/value: This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.
| Original language | English (US) |
|---|---|
| Journal | Internet Research |
| DOIs | |
| State | Accepted/In press - Jan 1 2019 |
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All Science Journal Classification (ASJC) codes
- Communication
- Sociology and Political Science
- Economics and Econometrics
Cite this
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Classifying online corporate reputation with machine learning : a study in the banking domain. / Rantanen, Anette; Salminen, Joni; Ginter, Filip; Jansen, Bernard J.
In: Internet Research, 01.01.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Classifying online corporate reputation with machine learning
T2 - a study in the banking domain
AU - Rantanen, Anette
AU - Salminen, Joni
AU - Ginter, Filip
AU - Jansen, Bernard J.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Purpose: User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations. Design/methodology/approach: The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data. Findings: After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation. Practical implications: For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN. Originality/value: This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.
AB - Purpose: User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations. Design/methodology/approach: The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data. Findings: After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation. Practical implications: For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN. Originality/value: This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.
UR - http://www.scopus.com/inward/record.url?scp=85074938098&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074938098&partnerID=8YFLogxK
U2 - 10.1108/INTR-07-2018-0318
DO - 10.1108/INTR-07-2018-0318
M3 - Article
AN - SCOPUS:85074938098
JO - Internet Research
JF - Internet Research
SN - 1066-2243
ER -