dahouabdelghani/arabic_word_embeddings_CNN

Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification (Coling 2016)

Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification (CoLing 2016)

In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings.

• Paper: https://www.aclweb.org/anthology/C/C16/C16-1228.pdf
• Data on Baidu (In Chinese): http://pan.baidu.com/s/1eS2mxCe
• Data on Google drive: https://drive.google.com/open?id=0B2WzDD9FC2KXRHlYNjYxUmowRW8

Project Layout

Subdirectories:

• Arabic_WE_eval - Arabic Word Embeddings models evaluation using Arabic word analogies
• Arabic_WE_model - Arabic Word Embeddings models
• CNN - Convolutional Neural Network to train and evalute Arabic sentiment classification task

Requirements

• Keras 0.3.3
• Theano
• Cuda

Script is running on GPU

Output example on ASTD unbalanced dataset

                precision    recall  f1-score   support

             0       0.82      0.85      0.83       362
             1       0.66      0.61      0.64       178
    avg / total      0.77      0.77      0.77       540

Citation

Dahou, A., Xiong, S., Zhou, J., Haddoud, M. H., & Duan, P. Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification.