# Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction

arXiv: https://arxiv.org/abs/2001.01989 · HTML: https://mlsanity.com/paper/2001.01989/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge](https://mlsanity.com/paper/2105.02520/) (2021)
- [SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2109.08306/) (2021)
- [UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning](https://mlsanity.com/paper/2211.10986/) (2022)
- [A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task](https://mlsanity.com/paper/2103.15255/) (2021)
- [An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction](https://mlsanity.com/paper/2109.01238/) (2021)
- [It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance](https://mlsanity.com/paper/2405.20703/) (2024)
- [Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2208.08280/) (2022)
- [AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2211.03837/) (2022)
- [A semantically enhanced dual encoder for aspect sentiment triplet extraction](https://mlsanity.com/paper/2306.08373/) (2023)
- [Training Entire-Space Models for Target-oriented Opinion Words Extraction](https://mlsanity.com/paper/2204.07337/) (2022)
- [Aspect-specific Context Modeling for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2207.08099/) (2022)
- [Aspect-Sentiment-Multiple-Opinion Triplet Extraction](https://mlsanity.com/paper/2110.07303/) (2021)
- [Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement](https://mlsanity.com/paper/2305.11034/) (2023)
- [Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis](https://mlsanity.com/paper/2311.16678/) (2023)
- [BanglaASTE: A Novel Framework for Aspect-Sentiment-Opinion Extraction in Bangla E-commerce Reviews Using Ensemble Deep Learning](https://mlsanity.com/paper/2511.21381/) (2025)

## Abstract (arXiv)

Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.