# From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision

arXiv: https://arxiv.org/abs/2305.01710 · HTML: https://mlsanity.com/paper/2305.01710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018)
- [Solving Aspect Category Sentiment Analysis as a Text Generation Task](https://mlsanity.com/paper/2110.07310/) (2021)
- [Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews](https://mlsanity.com/paper/1802.07938/) (2018)
- [Exploiting Document Knowledge for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1806.04346/) (2018)
- [Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks](https://mlsanity.com/paper/1808.09238/) (2018)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [Embarrassingly Simple Unsupervised Aspect Extraction](https://mlsanity.com/paper/2004.13580/) (2020)
- [Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis](https://mlsanity.com/paper/2010.02656/) (2020)
- [W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/1705.07687/) (2017)
- [INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1609.02748/) (2016)
- [Evaluating topic coherence measures](https://mlsanity.com/paper/1403.6397/) (2014)
- [Survey of Aspect-based Sentiment Analysis Datasets](https://mlsanity.com/paper/2204.05232/) (2022)

## Abstract (arXiv)

Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection, aspect-category sentiment analysis, and rating prediction. However, independently tackling these tasks can overlook their interdependencies and often requires expensive, fine-grained annotations. This paper introduces unified sentiment analysis, a novel learning paradigm that integrates the three aforementioned tasks into a coherent framework. To achieve this, we propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels in a hierarchical manner. Evaluations on multi-aspect review datasets in English and Chinese show that DSPN, using only star rating labels for supervision, demonstrates significant efficiency advantages while performing comparably well to a variety of benchmark models. Additionally, DSPN's pyramid structure enables the interpretability of its outputs. Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.