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 areaNatural Language ProcessingMore specific subject areaArabic Sentiment EmbeddingsType of datatext filesHow data was acquiredTraining Tang’s [2] models on an Arabic tweets dataset that was independently collected.Data formatRawData source locationNot applicableData accessibilityValue 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.ReferencesN. Al-Twairesh, H. Al-Negheimish, Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets , in submission. 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).