# Snippext: Semi-supervised Opinion Mining with Augmented Data

arXiv: https://arxiv.org/abs/2002.03049 · HTML: https://mlsanity.com/paper/2002.03049/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 26 of 26)

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- [Prompted Aspect Key Point Analysis for Quantitative Review Summarization](https://mlsanity.com/paper/2407.14049/) (2024)
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- [Data Augmentation for Conversational AI](https://mlsanity.com/paper/2309.04739/) (2023)
- [Rationale-based Opinion Summarization](https://mlsanity.com/paper/2404.00217/) (2024)
- [Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2208.08280/) (2022)
- [Enhancing LLM Knowledge Learning through Generalization](https://mlsanity.com/paper/2503.03705/) (2025)
- [Reprint: a randomized extrapolation based on principal components for data augmentation](https://mlsanity.com/paper/2204.12024/) (2022)
- [Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime](https://mlsanity.com/paper/2305.09287/) (2023)
- [Out-of-Distribution Generalization in Text Classification: Past, Present, and Future](https://mlsanity.com/paper/2305.14104/) (2023)
- [Recent Trends in Unsupervised Summarization](https://mlsanity.com/paper/2305.11231/) (2023)
- [AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization](https://mlsanity.com/paper/2306.05537/) (2023)
- [SegMix: A Simple Structure-Aware Data Augmentation Method](https://mlsanity.com/paper/2311.09505/) (2023)
- [Data Augmentation Techniques for Process Extraction from Scientific Publications](https://mlsanity.com/paper/2405.14594/) (2024)
- [AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework](https://mlsanity.com/paper/2406.13947/) (2024)

## Abstract (arXiv)

Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain high-quality extractions from reviews. However, fine-tuning language models still requires a non-trivial amount of training data. In this paper, we study the problem of how to significantly reduce the amount of labeled training data required in fine-tuning language models for opinion mining. We describe Snippext, an opinion mining system developed over a language model that is fine-tuned through semi-supervised learning with augmented data. A novelty of Snippext is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data. We show with extensive experiments that Snippext performs comparably and can even exceed previous SOTA results on several opinion mining tasks with only half the training data required. Furthermore, it achieves new SOTA results when all training data are leveraged. By comparison to a baseline pipeline, we found that Snippext extracts significantly more fine-grained opinions which enable new opportunities of downstream applications.