Detecting Irony in Arabic Microblogs using Deep Convolutional Neural Networks

Alhaidari, Linah; Alyoubi, Khaled H.; Alotaibi, Fahd Saleh

doi:10.14569/ijacsa.2022.0130187

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Comparatives are shown Table 4. significant results and underscoring the potential of deep learning in identifying complex linguistic constructs like irony ("Detecting Irony in Arabic Microblogs using Deep Convolutional Neural Networks") [39]. Comparatives are shown Table 5.…”

Section: Arabic Word Embedding In Natural Language Processingmentioning

confidence: 89%

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Elshafy,

Aly,

Gheith

2024

IJRASET

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Sentiment analysis is a crucial component of natural language processing that seeks to determine the emotional sentiment expressed in a given text. This study investigates sentiment analysis in the Arabic language through a comprehensive approach that integrates traditional machine learning methods with sophisticated deep learning models. We examine the efficacy of conventional algorithms such as Support Vector Machines (SVM) and Naive Bayes, as well as sophisticated neural network architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and the Arabic variant of Bidirectional Encoder Representations from Transformers (BERT). The primary novelty of our approach is in the ensemble method, which combines many approaches to enhance the precision of sentiment categorization in Arabic text. To address the particular challenges presented by Arabic sentiment analysis, such as the intricate structure of the language and the diverse regional variations, we utilize a tailored preprocessing pipeline to effectively handle the nuances of Arabic text. Our comprehensive analysis of various datasets demonstrates that the ensemble technique outperforms individual model benchmarks and offers novel insights into the interplay between different machine learning paradigms in Arabic NLP. The results emphasize the ability of hybrid approaches to improve Arabic sentiment analysis, providing a solid basis for future research and practical applications in understanding the sentiments of Arabic consumers. This study is a significant addition to the expanding domain of Arabic Natural Language Processing (NLP). This resource offers a comprehensive and advanced methodology for utilizing machine learning and deep learning methods to comprehend and analyse the intricate aspects of sentiment in the Arabic language

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Section: Arabic Word Embedding In Natural Language Processingmentioning

confidence: 89%

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Elshafy,

Aly,

Gheith

2024

IJRASET

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“…The dataset includes different variations of Arabic such as MSA, dialectal Arabic, and a mix of both in some tweets. Finally, the DIAM dataset has been collected recently by the authors in [64]. We designated the name of DIAM to the dataset referring to Detecting Irony in Arabic Microblogs.…”

Section: Arabic Sarcasm Datasetsmentioning

confidence: 99%

“…The experiment was measured using a loss function with a final value of 0.011631458. A CNN and BILSTMbased model were introduced in [64]. Moreover, the authors collected a new corpus for Arabic irony detection, namely the DIAM.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends

2023

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On social media platforms, it is essential to express one's thoughts, opinions, and reviews. One of the most widely used linguistic forms to criticize or express a person's ideas with ridicule is sarcasm, where the written text has both intended and unintended meanings. The sarcastic text frequently reverses the polarity of the sentiment. Therefore, detecting sarcasm in the text has a positive impact on the sentiment analysis task and ensures more accurate results. Although Arabic is one of the most frequently used languages for web content sharing, the sarcasm detection of Arabic content is restricted and yet still naive due to several challenges, including the morphological structure of the Arabic language, the variety of dialects, and the lack of adequate data sources. Despite that, researchers started investigating this area by introducing the first Arabic dataset and experiment for irony detection in 2017. Thus, our review focuses on studies published between 2017 and 2022 on Arabic sarcasm detection. We provide a thorough literature review of Artificial Intelligence (AI) techniques and benchmarks used for Arabic sarcasm detection. In addition, the challenges of Arabic sarcasm detection are investigated, along with future directions, focusing on the challenge of publicly available Arabic sarcasm datasets.INDEX TERMS Artificial intelligence (AI), Arabic sarcasm detection, deep learning (DL), machine learning (ML), natural language processing (NLP), sentiment analysis (SA).

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“…MSA leverages massive multimodal data generated on social media for integrated analysis, combining various multimodal features. This approach not only enables a more comprehensive understanding of user emotional expressions but also effectively addresses limitations of single-modal methods in handling complex emotions, ambiguity, or extreme cases like irony [7].…”

Section: Introductionmentioning

confidence: 99%

Cross-Modal Sentiment Analysis Based on CLIP Image-Text Attention Interaction

Lu,

Ni,

Ding

2024

IJACSA

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Multimodal sentiment analysis is a traditional textbased sentiment analysis technique. However, the field of multimodal sentiment analysis still faces challenges such as inconsistent cross-modal feature information, poor interaction capabilities, and insufficient feature fusion. To address these issues, this paper proposes a cross-modal sentiment model based on CLIP image-text attention interaction. The model utilizes pre-trained ResNet50 and RoBERTa to extract primary image-text features. After contrastive learning with the CLIP model, it employs a multi-head attention mechanism for cross-modal feature interaction to enhance information exchange between different modalities. Subsequently, a cross-modal gating module is used to fuse feature networks, combining features at different levels while controlling feature weights. The final output is fed into a fully connected layer for sentiment recognition. Comparative experiments are conducted on the publicly available datasets MSVA-Single and MSVA-Multiple. The experimental results demonstrate that our model achieved accuracy rates of 75.38% and 73.95% , and F1-scores of 75.21% and 73.83% on the mentioned datasets, respectively. This indicates that the proposed approach exhibits higher generalization and robustness compared to existing sentiment analysis models.

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Detecting Irony in Arabic Microblogs using Deep Convolutional Neural Networks

Cited by 5 publications

References 22 publications

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends

Cross-Modal Sentiment Analysis Based on CLIP Image-Text Attention Interaction

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