ConTrip: Consensus Sentiment review Analysis and Platform ratings in a single score
This work addresses the challenge for consumers and platforms in interpreting review sentiments when ratings are identical, though it is incremental as it builds on prior consensus methods.
The authors tackled the problem of creating a consensus sentiment score from reviews that differentiates items with equal ratings, resulting in ConTrip, a novel score that merges a consensus value with platform ratings to improve interpretability and differentiation.
People unequivocally employ reviews to decide on purchasing an item or an experience on the internet. In that regard, the growing significance and number of opinions have led to the development of methods to assess their sentiment content automatically. However, it is not straightforward for the models to create a consensus value that embodies the agreement of the different reviews and differentiates across equal ratings for an item. Based on the approach proposed by Nguyen et al. in 2020, we derive a novel consensus value named ConTrip that merges their consensus score and the overall rating of a platform for an item. ConTrip lies in the rating range values, which makes it more interpretable while maintaining the ability to differentiate across equally rated experiences. ConTrip is implemented and freely available under MIT license at https://github.com/pepebonet/contripscore