Skip to content

rub-ksv/AdHominem

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AdHominem: A tool for automatically analyzing the writing style in social media messages

This repository contains the source code used in our paper Explainable Authorship Verification in Social Media via Attention-based Similarity Learning published at 2019 IEEE International Conference on Big Data (IEEE BigData 2019)

Please, feel free to send any comments or suggestions! (benedikt.boenninghoff[at]rub.de)

Installation

We used Python 3.6 (Anaconda 3.6). The following libraries are required:

  • Tensorflow 1.12.0
  • spacy 2.1.8
  • textacy 0.8.0
  • fasttext 0.9.1
  • numpy 1.15.4
  • scipy 1.1.0
  • pandas 0.23.4
  • scikit-learn 0.20.0
  • bs4 0.0.1

Dataset

The large-scale dataset of short Amazon reviews used in our paper will be published as soon as possible. Currently, this repository works with a small Amazon review dataset. You can download and uncompress the data as follows:

mkdir data
cd data
wget https://github.com/marjanhs/prnn/raw/master/data/amazon.7z
sudo apt-get install p7zip-full
7z x amazon.7z

Download pretrained word embeddings

We used pretrained word embeddings. You may prepare them as follows:

cd data
wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz
gunzip cc.en.300.bin.gz

Data preprocessing

cd preprocessing
python main_preprocess.py

Training

You can choose two Siamese network models: AdHominem or HRSN:

cd training
python main.py --model_type "AdHominem"

Cite the paper

If you use our code or data, please cite the papers using the following BibTeX entries:

@inproceedings{Boenninghoff2019b,
author={Benedikt Boenninghoff, Steffen Hessler, Dorothea Kolossa and Robert M. Nickel},
title={Explainable Authorship Verification in Social Media via Attention-based Similarity Learning},
booktitle={IEEE International Conference on Big Data (IEEE Big Data 2019), Los Angeles, CA, USA, December 9-12, 2019},
year={2019},
}

@inproceedings{Boenninghoff2019a,
author={Benedikt Boenninghoff, Robert M. Nickel, Steffen Zeiler and Dorothea Kolossa},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), Brighton, UK, May 12-17, 2019},
title={Similarity Learning for Authorship Verification in Social Media},
year={2019},
pages={2457-2461},
doi={10.1109/ICASSP.2019.8683405},
}

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%