# Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

arXiv: https://arxiv.org/abs/2101.10150 · HTML: https://mlsanity.com/paper/2101.10150/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://mlsanity.com/paper/1808.04339/) (2018)
- [Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations](https://mlsanity.com/paper/1705.08841/) (2017)
- [Topic Modeling with Wasserstein Autoencoders](https://mlsanity.com/paper/1907.12374/) (2019)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [Neural Topic Modeling with Bidirectional Adversarial Training](https://mlsanity.com/paper/2004.12331/) (2020)
- [ATM:Adversarial-neural Topic Model](https://mlsanity.com/paper/1811.00265/) (2018)
- [Text-Based Ideal Points](https://mlsanity.com/paper/2005.04232/) (2020)
- [Automatic Labelling of Topics with Neural Embeddings](https://mlsanity.com/paper/1612.05340/) (2016)
- [Variational Deep Semantic Hashing for Text Documents](https://mlsanity.com/paper/1708.03436/) (2017)
- [Neural Topic Modeling with Cycle-Consistent Adversarial Training](https://mlsanity.com/paper/2009.13971/) (2020)

## Built on by (most cited first, 1 of 1)

- [Tracking Brand-Associated Polarity-Bearing Topics in User Reviews](https://mlsanity.com/paper/2301.07183/) (2023)

## Abstract (arXiv)

In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as `positive', `negative' and `neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., `shaver' or `cream') while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polarity-bearing topics.