Abstract:
Purpose – Social media channels provide an avenue for expressing views about different services/products.
However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not
possible to understand user’s ratings for a particular service-related comment on social media unless explicitly
mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally,
predicting ratings from a user-generated content can help in developing vast data sets for recommender
systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the
performance of sentiment-based predictors by combining it with the emotional content of textual data.
Design/methodology/approach – This study had utilized a combination of sentiment and emotion scores to
predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD)
services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized
for training and testing purposes.
Findings – Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA
and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in
accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.
Practical implications – Understanding the ratings of social media comments can help both service
providers as well as prospective customers who do not spend much time reading posts but want to understand
the perspectives of others about a particular service/product. Additionally, predicting ratings of social media
comments will help to build databases for recommender systems in different contexts.
Originality/value – The uniqueness of this study is in utilizing a combination of sentiment and emotion
scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.