Arabic Sentiment Embeddings

Includes sentiment-specific distributed word representations that have been trained on 10M Arabic tweets that are distantly supervised using positive and negative keywords. As described in the paper [1], we follow Tang’s [2] three neural architectures, which encode the sentiment of a word in addition to its semantic and syntactic representation. 

Specifications Table

Subject area

 Natural Language Processing

More specific subject area

Arabic Sentiment Embeddings

Type of data

text files

How data was acquired

Training Tang’s [2] models on an Arabic tweets dataset that was independently collected.

Data format

Raw

Data source location

Not applicable

Data accessibility

Value of the data  

·        May replace hand-engineered features for sentiment classification.

·        Can be used for benchmarking other Arabic sentiment embeddings.

·        The Arabic sentiment embeddings can be used for other NLP tasks where sentiment is important.

References

1. N. Al-Twairesh, H. Al-Negheimish, Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets , in submission.
2. D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, B. Qin, Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification, in: Proc. 52nd Annu. Meet. Assoc. Comput. Linguist. Vol. 1 Long Pap., Association for Computational Linguistics, Baltimore, Maryland, 2014: pp. 1555–1565. http://www.aclweb.org/anthology/P14-1146 (accessed May 18, 2018).