https://github.com/lyuchenyang/personalized-sentiment-analysis
Code for Findings of ACL 2023 paper "Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis"
https://github.com/lyuchenyang/personalized-sentiment-analysis
deep-learning machine-learning natural-language-processing natural-language-understanding sentiment-analysis sentiment-classification
Last synced: about 1 month ago
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Code for Findings of ACL 2023 paper "Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis"
- Host: GitHub
- URL: https://github.com/lyuchenyang/personalized-sentiment-analysis
- Owner: lyuchenyang
- License: apache-2.0
- Created: 2023-06-29T11:26:36.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-04T11:10:38.000Z (almost 2 years ago)
- Last Synced: 2025-01-22T08:45:04.864Z (3 months ago)
- Topics: deep-learning, machine-learning, natural-language-processing, natural-language-understanding, sentiment-analysis, sentiment-classification
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis
**[Chenyang Lyu](https://lyuchenyang.github.io), [Linyi Yang](mailto:[email protected]), [Yue Zhang](mailto:[email protected]), [Yvette Graham](mailto:[email protected]), [Jennifer Foster](mailto:[email protected])**
School of Computing, Dublin City University, Dublin, Ireland 🏠
School of Engineering, Westlake University, China;
School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
## Introduction 📝
This repository contains the code and resources for our Findings of ACL 2023 paper titled "Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis". In this paper, we propose a novel approach to improve personalized sentiment analysis by leveraging rich textual user-product context.
## Installation 📋
To get started, please install the required libraries by running the following command:
```angular2
pip install -r requirements.txt
```
## Download datasets 📥
Next, download the datasets from the following URL: [dataset](https://drive.google.com/file/d/1Bdt_jw-kiZCt7vJyfXe1hYmPKMinbtFu/view?usp=sharing).
Unzip the downloaded zip file and move all dataset files to the "data/personalized-sa/" directory.
## Training 🚀
To train the model, use the following code:
```
python run_cross_context_sa.py --task_name yelp-2013 \
--model_type bert \
--model_size base \
--epochs 5 \
--do_train \
--weight_decay 0.0 \
--learning_rate 5e-5 \
--warmup_steps 0.2 \
--max_seq_length 512 \
```
## Evaluation 📊
To evaluate a trained model with the specified parameters, use the following code:
```
python run_cross_context_sa.py --task_name yelp-2013 \
--model_type bert \
--model_size base \
--epochs 5 \
--do_eval \
--weight_decay 0.0 \
--learning_rate 5e-5 \
--warmup_steps 0.2 \
--max_seq_length 512 \
```
## License 📄
This work is licensed under a [Creative Commons Attribution 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/).
## Citation 📄
Please cite our paper using the bibtex below if you found that our paper is useful to you:
```bibtex
@article{lyu2022exploiting,
title={Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis},
author={Lyu, Chenyang and Yang, Linyi and Zhang, Yue and Graham, Yvette and Foster, Jennifer},
journal={arXiv preprint arXiv:2212.08888},
year={2022}
}
```