# A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification

arXiv: https://arxiv.org/abs/1904.05055 · HTML: https://mlsanity.com/paper/1904.05055/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Weakly Supervised Text Classification using Supervision Signals from a Language Model](https://mlsanity.com/paper/2205.06604/) (2022)
- [BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis](https://mlsanity.com/paper/2203.11702/) (2022)
- [SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2105.12305/) (2021)
- [Variational Weakly Supervised Sentiment Analysis with Posterior Regularization](https://mlsanity.com/paper/2104.08779/) (2021)

## Abstract (arXiv)

In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as "supervision." These word pairs can be extracted by using dependency parsers and simple rules. Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i.e., the sentiment polarity, to the objective function, we can inject the sentiment polarity classifier to the objective via the variational lower bound. We can learn a sentiment polarity classifier by optimizing the lower bound. We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect.