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

Dataset Files

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Documentation: 
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PDF icon Arabic Sentiment Embeddings.pdf350.35 KB
[1] , "Arabic Sentiment Embeddings", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/aavk-g896. Accessed: Aug. 24, 2019.
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doi = {10.21227/aavk-g896},
url = {http://dx.doi.org/10.21227/aavk-g896},
author = { },
publisher = {IEEE Dataport},
title = {Arabic Sentiment Embeddings},
year = {2019} }
TY - DATA
T1 - Arabic Sentiment Embeddings
AU -
PY - 2019
PB - IEEE Dataport
UR - 10.21227/aavk-g896
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. (2019). Arabic Sentiment Embeddings. IEEE Dataport. http://dx.doi.org/10.21227/aavk-g896
, 2019. Arabic Sentiment Embeddings. Available at: http://dx.doi.org/10.21227/aavk-g896.
. (2019). "Arabic Sentiment Embeddings." Web.
1. . Arabic Sentiment Embeddings [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/aavk-g896
. "Arabic Sentiment Embeddings." doi: 10.21227/aavk-g896