https://github.com/iamaziz/ar-embeddings

Sentiment Analysis for Arabic Text (tweets, reviews, and standard Arabic) using word2vec
https://github.com/iamaziz/ar-embeddings

arabic arabic-embedding arabic-nlp arabic-sentiment embeddings sentiment-analysis word2vec word2vec-model

Last synced: 18 days ago
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Sentiment Analysis for Arabic Text (tweets, reviews, and standard Arabic) using word2vec

• Host: GitHub
• URL: https://github.com/iamaziz/ar-embeddings
• Owner: iamaziz
• License: mit
• Created: 2016-11-15T23:06:28.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2024-08-20T16:25:54.000Z (4 months ago)
• Last Synced: 2024-11-16T23:03:16.145Z (26 days ago)
• Topics: arabic, arabic-embedding, arabic-nlp, arabic-sentiment, embeddings, sentiment-analysis, word2vec, word2vec-model
• Language: Python
• Homepage: https://huggingface.co/azizalto/arabic-news-embeddings
• Size: 3.83 MB
• Stars: 90
• Watchers: 10
• Forks: 47
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-arabic-nlp - iamaziz/ar-embeddings

README

        
Code, embeddings, and datasets used in the paper:

A. Altowayan and L. Tao _"Word Embeddings for Arabic Sentiment Analysis"_, IEEE BigData 2016 Workshop

##### How to run:

Make sure to unzip `embeddings/arabic-news.tar.gz`, then run

`$ python asa.py --vectors embeddings/arabic-news.bin --dataset datasets/LABR-book-reviews.csv`

```

[2017-04-08 12:59:20,387] INFO: loading projection weights from embeddings/arabic-news.bin

[2017-04-08 12:59:23,408] INFO: loaded (159175, 300) matrix from embeddings/arabic-news.bin

[2017-04-08 12:59:23,408] INFO: precomputing L2-norms of word weight vectors

[2017-04-08 12:59:24,525] INFO: dataset datasets/LABR-book-reviews.csv (16448, 2). Split: 14803 training and 1645 testing.

[2017-04-08 12:59:24,526] INFO: Tokenizing the training dataset ..

[2017-04-08 12:59:24,950] INFO:  ... total 927007 training tokens.

[2017-04-08 12:59:24,950] INFO: Tokenizing the testing dataset ..

[2017-04-08 12:59:25,003] INFO:  ... total 110705 testing tokens.

[2017-04-08 12:59:25,003] INFO: Vectorizing training tokens ..

[2017-04-08 12:59:27,414] INFO:  ... total 14803 training

[2017-04-08 12:59:27,415] INFO: Vectorizing testing tokens ..

[2017-04-08 12:59:27,723] INFO:  ... total 1645 testing

[2017-04-08 12:59:27,848] INFO: Done loading and vectorizing data.

[2017-04-08 12:59:27,848] INFO: --- Sentiment CLASSIFIERS ---

[2017-04-08 12:59:27,848] INFO: fitting ...

[2017-04-08 13:02:03,397] INFO: results ...

	MacAvg. 80.41% F1. 79.95% P. 81.37 R. 78.58 : LinearSVC

	MacAvg. 77.31% F1. 76.79% P. 78.10 R. 75.52 : RandomForestClassifier

	MacAvg. 63.93% F1. 57.42% P. 72.88 R. 47.37 : GaussianNB

	MacAvg. 80.84% F1. 80.50% P. 81.45 R. 79.56 : NuSVC

	MacAvg. 81.15% F1. 80.77% P. 81.89 R. 79.68 : LogisticRegressionCV

	MacAvg. 78.97% F1. 79.00% P. 78.34 R. 79.68 : SGDClassifier

[2017-04-08 13:02:03,397] INFO: DONE!

```

##### Dependencies:

Check out `requirements.txt` file.

To install the dependencies:

> `$ pip install -r requirements.txt`