Helpfulness Measurement

Note: the code and details of the framework will be released recently.

The Helpfulness Measurement (HelpMe) framework provides an all-in-one solution for generic review helpfulness prediction tasks. The framework consists of a series of built-in functions (e.g., review pre-processing, review labeling, review splitting, feature extraction, and embedding training) that facilitate data processing, model prototyping, and baseline evaluation. At present, the framework has integrated a comprehensive list of proposed features and prediction models and will continue to include more published results.

HelpMe fills the gap that existing studies lack systematic experiment standards and reproducible implementations. Future researchers are welcome and encouraged to adopt the framework for data preparation and model development to achieve a higher level of result comparability and reproducibility.

Please cite one or both of the following papers if you use the code for any purposes:

@article{Du2019Selection,
    author = {Du, Jiahua and Rong, Jia and Michalska, Sandra and Wang, Hua and Zhang, Yanchun},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Feature Selection for Helpfulness Prediction of Online Product Reviews: An Empirical Study},
    year = {2019},
    month = {December},
    volume = {14},
    url = {https://doi.org/10.1371/journal.pone.0226902},
    pages = {1--26},
    number = {12},
    doi = {10.1371/journal.pone.0226902}
}

@InProceedings{Du2019ECRI,
    author={Du, Jiahua and Rong, Jia and Wang, Hua and Zhang, Yanchun},
    title={Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction},
    booktitle={Web Information Systems Engineering (WISE)},
    month={October},
    year={2019},
    publisher={Springer International Publishing},
    address={Hong Kong SAR, China},
    pages={795--809},
    isbn={978-3-030-34223-4}
}