Semantics Preserving Emoji Recommendation with Large Language Models

Semantics Preserving Emoji Recommendation with Large Language Models

Zhongyi Qiu, Kangyi Qiu, Hanjia Lyu, Wei Xiong, Jiebo Luo

Accepted for publication in IEEE BigData 2024

Contact

Zhongyi Qiu (zhongyiqiu@gatech.edu), Kangyi Qiu (kqiu37@gatech.edu), Hanjia Lyu (hlyu5@ur.rochester.edu), Wei Xiong (wxiongur@gmail.com), Jiebo Luo (jluo@cs.rochester.edu)

Environment Setup for Emoji Recommendation Project

Prerequisites

1. Ensure Conda is installed. Install Conda if not already available.
2. Ensure requirements.txt is in the same directory as setup_env.sh.

Steps to Set Up the Environment

1. Run the setup script:
   Use the following command to run the setup script:

   bash setup_env.sh

   Or:

   sh setup_env.sh

   This creates the emoji_recommendation environment, installs Python 3.10, and installs dependencies from requirements.txt.

2. Activate the environment:

   conda activate emoji_recommendation

Folder Descriptions

• Code:

  • The src folder contains all the Jupyter Notebook (.ipynb) files used for data preprocessing and running the models. These notebooks handle tasks such as preparing the dataset, running the emoji recommendation models, and evaluating the results.

• Dataset:

  • src/dataset: Contains the initial raw dataset used for preprocessing and analysis.
  • src/dataset_finalOutput: Stores the output results generated by six large language models (LLMs).
  • src/human_eval: Includes a small subset of manually labeled data used for evaluating the model predictions.

Citation

@article{qiu2024semantics,
  title={Semantics Preserving Emoji Recommendation with Large Language Models},
  author={Qiu, Zhongyi and Qiu, Kangyi and Lyu, Hanjia and Xiong, Wei and Luo, Jiebo},
  journal={arXiv preprint arXiv:2409.10760},
  year={2024}
}

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[ACL 2024] SoMeLVLM: A Large Vision Language Model for Social Media Processing

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