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Persian Sentence-level Sentiment Polarity Classification
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In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021)
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Persian Sentence-level Sentiment Polarity Classification
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In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021)
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Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis
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Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
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A novel context-aware multimodal framework for persian sentiment analysis
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A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
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A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect
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A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
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Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
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CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement
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Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
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Deep Neural Network Driven Binaural Audio Visual Speech Separation
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Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System
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A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks
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A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks ...
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Lip-reading driven deep learning approach for speech enhancement
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In: abs/1808.00046 ; 1 ; 10 (2019)
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A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks
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Abstract:
Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment analysis approaches are mainly based on word co-occurrence frequencies, which are inadequate in most practical cases. In this work, we propose a novel hybrid framework for concept-level sentiment analysis in Persian language, that integrates linguistic rules and deep learning to optimize polarity detection. When a pattern is triggered, the framework allows sentiments to flow from words to concepts based on symbolic dependency relations. When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification. The proposed framework outperforms state-of-the-art approaches (including support vector machine, and logistic regression) and DNN classifiers (long short-term memory, and Convolutional Neural Networks) with a margin of 10–15% and 3–4% respectively, using benchmark Persian product and hotel reviews corpora. ; Output Status: Forthcoming/Available Online
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Keyword:
Deep Learning; Dependency-based Rules; Low-Resource Natural Language Processing; Persian Sentiment Analysis
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URL: https://doi.org/10.1016/j.neucom.2019.10.009 http://hdl.handle.net/1893/30451 http://dspace.stir.ac.uk/bitstream/1893/30451/1/1-s2.0-S0925231219313815-main.pdf
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