@inproceedings{el-beltagy-etal-2017-niletmrg,
title = "{N}ile{TMRG} at {S}em{E}val-2017 Task 4: {A}rabic Sentiment Analysis",
author = "El-Beltagy, Samhaa R. and
El Kalamawy, Mona and
Soliman, Abu Bakr",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2133",
doi = "10.18653/v1/S17-2133",
pages = "790--795",
abstract = "This paper describes two systems that were used by the NileTMRG for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Subtask B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification). For subtask A, we made use of NU{'}s sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network that used trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one.",
}
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<abstract>This paper describes two systems that were used by the NileTMRG for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Subtask B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification). For subtask A, we made use of NU’s sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network that used trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one.</abstract>
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%0 Conference Proceedings
%T NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis
%A El-Beltagy, Samhaa R.
%A El Kalamawy, Mona
%A Soliman, Abu Bakr
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F el-beltagy-etal-2017-niletmrg
%X This paper describes two systems that were used by the NileTMRG for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Subtask B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification). For subtask A, we made use of NU’s sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network that used trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one.
%R 10.18653/v1/S17-2133
%U https://aclanthology.org/S17-2133
%U https://doi.org/10.18653/v1/S17-2133
%P 790-795
Markdown (Informal)
[NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis](https://aclanthology.org/S17-2133) (El-Beltagy et al., SemEval 2017)
ACL
- Samhaa R. El-Beltagy, Mona El Kalamawy, and Abu Bakr Soliman. 2017. NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 790–795, Vancouver, Canada. Association for Computational Linguistics.