An Efficient Procedure for Identifying the Similarity Between French and English Languages with Sequence Matcher Technique

Nag, Manish; Srinivas, G.; Rao, K. Venkata; Vakkalanka, Sairam; Nagendram, S.

doi:10.1007/978-981-16-5685-9_4

Cited by 3 publications

References 8 publications

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Automatic Algerian Sarcasm Detection from Texts and Images

Bousmaha,

Hamadouche,

Djouabi

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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In recent years, the number of Algerian Internet users has significantly increased, providing a valuable opportunity for collecting and utilizing opinions and sentiments expressed online. They now post not just texts but also images. However, to benefit from this wealth of information, it is crucial to address the challenge of sarcasm detection, which poses a limitation in sentiment analysis. Sarcasm often involves the use of non-literal and ambiguous language, making its detection complex. To enhance the quality and relevance of sentiment analysis, it is essential to develop effective methods for sarcasm detection. By overcoming this limitation, we can fully harness the expressed online opinions and benefit from their valuable insights for a better understanding of trends and sentiments among the Algerian public. In this work, our aim is to develop a comprehensive system that addresses sarcasm detection in Algerian dialect, encompassing both text and image analysis. We propose a hybrid approach that combines linguistic characteristics and machine learning techniques for text analysis. Additionally, for image analysis, we utilized the deep learning model VGG-19 for image classification, and employed the EasyOCR technique for Arabic text extraction. By integrating these approaches, we strive to create a robust system capable of detecting sarcasm in both textual and visual content in the Algerian dialect. Our system achieved an accuracy of 92.79% for the textual models and 89.28% for the visual model.

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Automatic Algerian Sarcasm Detection from Texts and Images

Bousmaha,

Hamadouche,

Djouabi

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Disaster tweet classification: A majority voting approach using machine learning algorithms

Krishna

Srinivas

P.V.G.D.

2022

Preprint

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Nowadays, People share their opinions through social media. This information may be informative or non-informative. To filtering the informative information from the social media plays a challenging issue. Nevertheless, in social media especially when a disaster been occurs the peoples will interact more on that particular disaster event. They share their opinion through some textual information such as tweets or posts. In this work, we are proposing a generalized approach for categorizing the informative and non-informative on twitter media. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events which contains the people’s opinions on that specific event. We preprocess the information which converts the tweet information into machine understandable vectors. These vectors been processed by the different machine learning algorithms. We consider the individual performance of each ML algorithm on different disaster datasets upon chosen the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall and f1-score. We compared our results with existing models in which our proposed model performed better than other existing state of art models.

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Disaster tweet classification: A majority voting approach using machine learning algorithms

Krishna

Srinivas

Reddy

2023

IDT

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Nowadays, people share their opinions through social media. This information may be informative or non-informative. Filtering informative information from social media plays a challenging issue. Nevertheless, people will interact more with that particular disaster event on social media, primarily when a disaster occurs. They share their opinion through some textual information such as tweets or posts. In this work, we propose a generalized approach for categorizing the informative and non-informative media on Twitter. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events containing people’s opinions on that specific event. We pre-process the information, which converts the tweet information into machine-understandable vectors. Various machine learning algorithms have processed these vectors. We consider the individual performance of each ML algorithm on different disaster datasets upon choosing the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall, and F1-score. We compared our results with existing models in which our proposed model performed better than other existing state of the art models.

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An Efficient Procedure for Identifying the Similarity Between French and English Languages with Sequence Matcher Technique

Cited by 3 publications

References 8 publications

Automatic Algerian Sarcasm Detection from Texts and Images

Automatic Algerian Sarcasm Detection from Texts and Images

Disaster tweet classification: A majority voting approach using machine learning algorithms

Disaster tweet classification: A majority voting approach using machine learning algorithms

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