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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 – 19 of 19 1 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander... In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021) BASE Show details 2 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander... In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021) BASE Show details 3 Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : Springer, 2021 BASE Show details 4 Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts Ahmed, Rami; Gogate, Mandar; Tahir, Ahsen. - : MDPI, 2021 BASE Show details 5 A novel context-aware multimodal framework for persian sentiment analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier, 2021 BASE Show details 6 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 7 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 8 A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier BV, 2021 BASE Show details 9 Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts Ahmed, Rami; Gogate, Mandar; Tahir, Ahsen. - : MDPI, 2021 BASE Show details 10 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : IEEE, 2021. : Piscataway, NJ, USA, 2021 BASE Show details 11 CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement Gogate, Mandar; Dashtipour, Kia; Adeel, Ahsan. - : Elsevier, 2020 BASE Show details 12 Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances Ahmed, Rami; Dashtipour, Kia; Gogate, Mandar. - : Springer, 2020 BASE Show details 13 Deep Neural Network Driven Binaural Audio Visual Speech Separation Gogate, Mandar; Dashtipour, Kia; Bell, Peter. - : Institute of Electrical and Electronics Engineers, 2020 BASE Show details 14 Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System Gogate, Mandar; Dashtipour, Kia; Hussain, Amir. - 2020 BASE Show details 15 A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng. - : Elsevier, 2019 BASE Show details 16 A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks ... Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng. - : arXiv, 2019 BASE Show details 17 Lip-reading driven deep learning approach for speech enhancement Adeel, Ahsan; Gogate, Mandar; Hussain, Amir... In: abs/1808.00046 ; 1 ; 10 (2019) BASE Show details 18 A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng. - : Elsevier, 2019 BASE Show details 19 Persian Named Entity Recognition Dashtipour, Kia; Gogate, Mandar; Adeel, Ahsan. - : Institute of Electrical and Electronics Engineers Inc, 2017. : Piscataway, NJ, USA, 2017 BASE Show details

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