SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

Kanfoud, Mohamed Raouf; Bouramoul, Abdelkrim

doi:10.1007/s10844-022-00714-8

Cited by 12 publications

(4 citation statements)

References 45 publications

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“…The proposed strategy improves prediction accuracy while decreasing computational and analytical delays. Kanfoud and Bouramoul (2022) a novel representation strategy for multilingual sentiment analysis was developed. An algorithm deciphers the sentiment codes and generates a workable data set for the detection procedure.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

Baskar

Dhote²,

Dhote

et al. 2022

Expert Systems

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Natural language processing (NLP) is widely used in multi‐media real‐time applications for understanding human interactions through computer aided‐analysis. NLP is common in auto‐filling, voice recognition, typo‐checking applications, and so forth. Multilingual NLP requires vast data processing and interaction recognition features for leveraging content retrieval precision. To strengthen this concept, a predictive typological content retrieval method is introduced in this article. The proposed method maximizes and relies on distributed transfer learning for training multilingual interactions with pitch and tone features. The phonetic pronunciation and the previous content‐based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the previous processing instances. For this purpose, the auto‐fill and error correction data are augmented with the training and multilingual processing databases. Depending on the current prediction and previous content, the knowledge base is updated, and further training relies on this feature. Therefore, the proposed method accurately identifies the content across multilingual NLP models.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

Baskar

Dhote²,

Dhote

et al. 2022

Expert Systems

View full text Add to dashboard Cite

show abstract

“…However, the zero-shot classification method fails to achieve high accuracies in monolingual data compared to multilingual data. Kanfoud et al [32] introduced SentiCode, a novel method for sentiment analysis across multiple languag-es and domains. SentiCode employs a unified model using a singular level of abstraction and pseudocode, avoiding the need for separate models for each language.…”

Section: Multilingual Sentiment Analysismentioning

confidence: 99%

A Survey of Sentiment Analysis and Sarcasm Detection

Derbala Yacoub,

O. Slim,

Elsayed Aboutabl

2024

Int. j. electr. comput. eng. syst. (Online)

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In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data.

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“…This study provided the detail of datasets used for the experiments in terms of the efficiency and accuracy of the proposed models. Kanfoud and Bouramoul ( 2022 ) used standard language features for multilanguage sentiment analysis with high-accuracy results. The authors suggested that this is incredibly convenient for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Multilingual deep learning framework for fake news detection using capsule neural network

2023

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Fake news detection is an essential task; however, the complexity of several languages makes fake news detection challenging. It requires drawing many conclusions about the numerous people involved to comprehend the logic behind some fake stories. Existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge these challenges and deal with multilingual fake news detection, we present a semantic approach to the identification of fake news based on relational variables like sentiment, entities, or facts that may be directly derived from the text. Our model outperformed the state-of-the-art methods by approximately 3.97% for English to English, 1.41% for English to Hindi, 5.47% for English to Indonesian, 2.18% for English to Swahili, and 2.88% for English to Vietnamese language reviews on TALLIP fake news dataset. To the best of our knowledge, our paper is the first study that uses a capsule neural network for multilingual fake news detection.

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SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

Cited by 12 publications

References 45 publications

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

A Survey of Sentiment Analysis and Sarcasm Detection

Multilingual deep learning framework for fake news detection using capsule neural network

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