# Aspect Level Sentiment Classification with Deep Memory Network

arXiv: https://arxiv.org/abs/1605.08900 · HTML: https://mlsanity.com/paper/1605.08900/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2016.

## Built on by (most cited first, 30 of 82)

- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2112.02706/) (2021)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [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)
- [Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.03271/) (2021)
- [Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa](https://mlsanity.com/paper/2104.04986/) (2021)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.02714/) (2021)
- [Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting](https://mlsanity.com/paper/2107.05192/) (2021)
- [Adapting a Language Model While Preserving its General Knowledge](https://mlsanity.com/paper/2301.08986/) (2023)
- [Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media](https://mlsanity.com/paper/2304.03087/) (2023)
- [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation](https://mlsanity.com/paper/2305.15186/) (2023)
- [Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2201.04831/) (2022)
- [Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification](https://mlsanity.com/paper/2103.11794/) (2021)
- [A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2204.12674/) (2022)
- [METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets](https://mlsanity.com/paper/2209.13773/) (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)
- [Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2203.16369/) (2022)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [Exploiting Position Bias for Robust Aspect Sentiment Classification](https://mlsanity.com/paper/2105.14210/) (2021)
- [Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.10816/) (2021)
- [DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification](https://mlsanity.com/paper/2201.00989/) (2022)
- [Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild](https://mlsanity.com/paper/2101.09449/) (2021)
- [BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification](https://mlsanity.com/paper/2110.00171/) (2021)

## Abstract (arXiv)

We introduce a deep memory network for aspect level sentiment classification. Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect. Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiments on laptop and restaurant datasets demonstrate that our approach performs comparable to state-of-art feature based SVM system, and substantially better than LSTM and attention-based LSTM architectures. On both datasets we show that multiple computational layers could improve the performance. Moreover, our approach is also fast. The deep memory network with 9 layers is 15 times faster than LSTM with a CPU implementation.