Retail industry consists of the establishment of selling
consumer goods (i.e. technology, pharmaceuticals, food and
beverages, apparels and accessories, home improvement etc.)
and services (i.e. specialty and movies) to customers through
multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of
retail companies is crucial as it has many advantages, for instance,
it has been proven to impact generated revenues (Wang et al.,
2016). But, in order to be able to manage corporate reputation,
one has to be able to measure it, or, nowadays even better, listen
to relevant social signals that are out there on the public web. One
of the most extensive and widely used frameworks for measuring
corporate reputation is through conducting elaborated surveys with
respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and
will not allow to generate automatic alerts and quick and live
insights that are extremely needed in this era of internet. For
these purposes a social listening approach is needed that can
be tailored to online data such as consumer reviews as the main
data source. Online review datasets are a form of electronic
Word-of-Mouth (WOM) that, when a data source is picked that is
relevant to retail, commonly contain relevant information about
customers’ perceptions regarding products (Pookulangara, 2011)
and that are massively available.
The algorithm that we have built in our application provides
retailers with reputation scores for all variables that are deemed
to be relevant to retail in the model of Fombrun et al. (2015).
Examples of such variables for products and services are high
quality, good value, stands behind, and meets customer needs.
We propose a new set of subvariables with which these variables
can be operationalized for retail in particular. Scores are being
calculated using proportions of positive opinion pairs such as
or that have been designed per
variable. With these important insights extracted, companies can
act accordingly and proceed to improve their corporate reputation.
It is important to emphasize that, once the design is complete
and implemented, all processing can be performed completely
automatic and unsupervised.
The application makes use of a state of the art aspect-based
sentiment analysis (ABSA) framework because of ABSA’s ability to
generate sentiment scores for all relevant variables and aspects.
Since most online data is in open form and we deliberately want
to avoid labelling any data by human experts, the unsupervised
aspectator algorithm has been picked. It employs a lexicon to
calculate sentiment scores and uses syntactic dependency paths
to discover candidate aspects (Bancken et al., 2014).
We have applied our approach to a large number of online review
datasets that we sampled from a list of 50 top global retailers
according to National Retail Federation (2020), including both
offline and online operation, and that we scraped from trustpilot, a
public website that is well-known to retailers.
The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation
purposes by two independent annotators. The Kappa’s score on
this subset was 80%.