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Simple Search
Hits 1 – 11 of 11
1
Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English ...
Guellil, Imane
;
Adeel, Ahsan
;
Azouaou, Faical
. - : arXiv, 2021
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2
A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
Adeel, Ahsan
;
Guellil, Imane
;
Gogate, Mandar
. - : Springer, 2021
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3
A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect
Guellil, Imane
;
Adeel, Ahsan
;
Azouaou, Faical
. - 2021
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4
CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement
Gogate, Mandar
;
Dashtipour, Kia
;
Adeel, Ahsan
. - : Elsevier, 2020
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5
Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
Ahmed, Rami
;
Dashtipour, Kia
;
Gogate, Mandar
. - : Springer, 2020
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6
Lip-reading driven deep learning approach for speech enhancement
Adeel, Ahsan
;
Gogate, Mandar
;
Hussain, Amir
...
In: abs/1808.00046 ; 1 ; 10 (2019)
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7
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
Guellil, Imane
;
Adeel, Ahsan
;
Azouaou, Faical
. - : Springer, 2018
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8
A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter
Alqarafi, Abdulrahman
;
Adeel, Ahsan
;
Hawalah, Ahmed
. - : Springer, 2018
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9
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis ...
Guellil, Imane
;
Adeel, Ahsan
;
Azouaou, Faical
. - : arXiv, 2018
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10
A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter
Alqarafi, Abdulrahman
;
Adeel, Ahsan
;
Hawalah, Ahmed
;
Swingler, Kevin
;
Hussain, Amir
. - : Springer International Publishing, 2018. : Cham, Switzerland, 2018
Abstract:
In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%.
Keyword:
Computational Intelligence and Machine Learning
;
Saudi dialect
;
Sentiment analysis
;
Word embedding
URL:
https://doi.org/10.1007/978-3-030-00563-4_57
http://hdl.handle.net/1893/29408
http://dspace.stir.ac.uk/bitstream/1893/29408/1/Camera%20Ready%20Paper-Bics%20Abdulrahman%20Alqarafi.pdf
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11
Persian Named Entity Recognition
Dashtipour, Kia
;
Gogate, Mandar
;
Adeel, Ahsan
. - : Institute of Electrical and Electronics Engineers Inc, 2017. : Piscataway, NJ, USA, 2017
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