Abstract:
Purpose – The widespread acceptance of various social platforms has increased the number of users posting
about various services based on their experiences about the services. Finding out the intended ratings of social
media (SM) posts is important for both organizations and prospective users since these posts can help in
capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the serviceexperience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can
also help to build a database using recent comments for testing recommender algorithms in various scenarios.
Design/methodology/approach – In this study, the authors have predicted the ratings of SM posts using
linear (Na€ıve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations
of different features, sentiment scores and emotion scores.
Findings –Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed
better than the linear classifiers (Na€ıve Bayes, Max-entropy classifier). Results also show an improvement
of performance where the classifier was combined with sentiment and emotion scores. Introduction of the
feature “factors of importance” or “the latent factors” also show an improvement of the classifier
performance.
Originality/value – This study provides a new avenue of predicting ratings of SM feeds by the use of
machine learning algorithms along with a combination of different features like emotional aspects and latent
factors.