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.