# A Recurrent Neural Network for Sentiment Quantification

arXiv: https://arxiv.org/abs/1809.00836 · HTML: https://mlsanity.com/paper/1809.00836/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [QuaPy: A Python-Based Framework for Quantification](https://mlsanity.com/paper/2106.11057/) (2021)
- [A Comparative Evaluation of Quantification Methods](https://mlsanity.com/paper/2103.03223/) (2021)
- [Binary Quantification and Dataset Shift: An Experimental Investigation](https://mlsanity.com/paper/2310.04565/) (2023)
- [Quantification using Permutation-Invariant Networks based on Histograms](https://mlsanity.com/paper/2403.15123/) (2024)
- [LeQua@CLEF2022: Learning to Quantify](https://mlsanity.com/paper/2111.11249/) (2021)
- [Regularization-Based Methods for Ordinal Quantification](https://mlsanity.com/paper/2310.09210/) (2023)
- [Multi-Label Quantification](https://mlsanity.com/paper/2211.08063/) (2022)
- [Class-Conditioned Gaussian Mixture Modeling for Imbalanced Time Series Quantification](https://mlsanity.com/paper/2608.21473/) (2026)

## Abstract (arXiv)

Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have been attributed to each class. However, this "classify and count" approach has been shown to yield suboptimal quantification accuracy; this has established quantification as a task of its own, and given rise to a number of methods specifically devised for it. We propose a recurrent neural network architecture for quantification (that we call QuaNet) that observes the classification predictions to learn higher-order "quantification embeddings", which are then refined by incorporating quantification predictions of simple classify-and-count-like methods. We test {QuaNet on sentiment quantification on text, showing that it substantially outperforms several state-of-the-art baselines.