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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 A Sentiment analysis approach for Arabic dialects texts analysis based on automatic translation: Application to the Algerian dialect. ... Guellil, Imane. - : Unpublished, 2021 BASE Show details 2 Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English ...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 BASE Show details 3 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 BASE Show details 4 Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Guellil, Imane. - : Underline Science Inc., 2021 BASE Show details 5 A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect Guellil, Imane; Adeel, Ahsan; Azouaou, Faical; Benali, Fodil; Hachani, Ala-Eddine; Dashtipour, Kia; Gogate, Mandar; Ieracitano, Cosimo; Kashani, Reza; Hussain, Amir. - 2021 Abstract: Abstract: In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%). URL: https://publications.aston.ac.uk/id/eprint/42339/ https://doi.org/10.1007/s42979-021-00510-1 https://publications.aston.ac.uk/id/eprint/42339/1/42979_2021_Article_510.pdf BASE Hide details

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