# Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

arXiv: https://arxiv.org/abs/2301.07183 · HTML: https://mlsanity.com/paper/2301.07183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019)
- [Topic Modeling with Wasserstein Autoencoders](https://mlsanity.com/paper/1907.12374/) (2019)
- [The Dynamic Embedded Topic Model](https://mlsanity.com/paper/1907.05545/) (2019)
- [MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation](https://mlsanity.com/paper/2007.03183/) (2020)
- [Dynamic Memory Induction Networks for Few-Shot Text Classification](https://mlsanity.com/paper/2005.05727/) (2020)
- [Text-Based Ideal Points](https://mlsanity.com/paper/2005.04232/) (2020)
- [Topic-Guided Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2110.11207/) (2021)
- [Personalized Adaptive Meta Learning for Cold-start User Preference Prediction](https://mlsanity.com/paper/2012.11842/) (2020)
- [HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset](https://mlsanity.com/paper/2002.06854/) (2020)
- [Dynamic Poisson Factorization](https://mlsanity.com/paper/1509.04640/) (2015)
- [Recurrent Poisson Factorization for Temporal Recommendation](https://mlsanity.com/paper/1703.01442/) (2017)
- [TOUR: Dynamic Topic and Sentiment Analysis of User Reviews for Assisting App Release](https://mlsanity.com/paper/2103.15774/) (2021)
- [Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews](https://mlsanity.com/paper/2101.10150/) (2021)

## Abstract (arXiv)

Monitoring online customer reviews is important for business organisations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organised in temporally-ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.