# Interactive Attention Networks for Aspect-Level Sentiment Classification

arXiv: https://arxiv.org/abs/1709.00893 · HTML: https://mlsanity.com/paper/1709.00893/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2017.

## Built on by (most cited first, 30 of 91)

- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (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)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (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)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2201.04831/) (2022)
- [Causal Intervention Improves Implicit Sentiment Analysis](https://mlsanity.com/paper/2208.09329/) (2022)
- [Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2406.08068/) (2024)
- [Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer](https://mlsanity.com/paper/2103.12360/) (2021)
- [A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.07832/) (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)
- [Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2203.16369/) (2022)
- [Exploiting Position Bias for Robust Aspect Sentiment Classification](https://mlsanity.com/paper/2105.14210/) (2021)
- [First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2102.08549/) (2021)
- [Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.10816/) (2021)
- [LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation](https://mlsanity.com/paper/2110.08604/) (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)
- [A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts](https://mlsanity.com/paper/2205.04185/) (2022)
- [Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection](https://mlsanity.com/paper/2310.01430/) (2023)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)

## Abstract (arXiv)

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.