# Exploiting Document Knowledge for Aspect-level Sentiment Classification

arXiv: https://arxiv.org/abs/1806.04346 · HTML: https://mlsanity.com/paper/1806.04346/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

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

- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives](https://mlsanity.com/paper/2403.14341/) (2024)
- [A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2204.12674/) (2022)
- [Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network](https://mlsanity.com/paper/2108.02352/) (2021)
- [Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2110.13398/) (2021)
- [Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2405.05496/) (2024)
- [Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.10816/) (2021)
- [Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis](https://mlsanity.com/paper/2203.07090/) (2022)
- [Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2407.00341/) (2024)
- [AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2211.03837/) (2022)
- [Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition](https://mlsanity.com/paper/2209.02276/) (2022)
- [SumHiS: Extractive Summarization Exploiting Hidden Structure](https://mlsanity.com/paper/2406.08215/) (2024)
- [Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification](https://mlsanity.com/paper/2509.11604/) (2025)
- [Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2111.14988/) (2021)
- [Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention](https://mlsanity.com/paper/2202.05457/) (2022)
- [From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision](https://mlsanity.com/paper/2305.01710/) (2023)
- [Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2312.03739/) (2023)

## Abstract (arXiv)

Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document- level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.