# Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification

arXiv: https://arxiv.org/abs/1909.06276 · HTML: https://mlsanity.com/paper/1909.06276/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 22 of 22)

- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2305.12627/) (2023)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2201.04831/) (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)
- [Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2306.00418/) (2023)
- [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)
- [A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions](https://mlsanity.com/paper/2305.07665/) (2023)
- [Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2407.00341/) (2024)
- [Exploring Model Editing for LLM-based Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2503.15117/) (2025)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [Collaborative Knowledge Infusion for Low-resource Stance Detection](https://mlsanity.com/paper/2403.19219/) (2024)
- [Interventional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2104.11681/) (2021)
- [Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2211.05457/) (2022)
- [Ad Text Classification with Transformer-Based Natural Language Processing Methods](https://mlsanity.com/paper/2106.10899/) (2021)
- [Knowledge Graph Enhanced Aspect-Level Sentiment Analysis](https://mlsanity.com/paper/2312.10048/) (2023)
- [KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering](https://mlsanity.com/paper/2407.00342/) (2024)
- [When Distributions Shifts: Causal Generalization for Low-Resource Languages](https://mlsanity.com/paper/2510.27512/) (2025)
- [From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning](https://mlsanity.com/paper/2604.13398/) (2026)

## Abstract (arXiv)

We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspect-specific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.