# ConTrip: Consensus Sentiment review Analysis and Platform ratings in a single score

arXiv: https://arxiv.org/abs/2201.02113 · HTML: https://mlsanity.com/paper/2201.02113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction](https://mlsanity.com/paper/1906.01236/) (2019)
- [A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews](https://mlsanity.com/paper/2010.11384/) (2020)

## Abstract (arXiv)

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