Multilingual hope speech detection: A Robust framework using transfer learning of fine-tuning RoBERTa model

Malik, Muhammad Shahid Iqbal; Nazarova, Anna; Jamjoom, Mona Mamdouh; Ignatov, Dmitry I.

doi:10.1016/j.jksuci.2023.101736

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…The classifiers are random forest (RF), logistic regression (LR), support vector machine (SVM), CNN, LSTM, and Bi-LSTM. The reason why we chose these ML and DL models is that they presented a significant performance in similar NLP and text mining tasks ( Malik et al, 2023 ; Rehan, Malik & Jamjoom, 2023 ). The following comparable models are designed:…”

Section: Methodsmentioning

confidence: 99%

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Malik,

Younas,

Jamjoom

et al. 2024

PeerJ Computer Science

Self Cite

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Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the Bidirectional Encoder Representations from Transformer (BERT) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.

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Section: Methodsmentioning

confidence: 99%

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Malik,

Younas,

Jamjoom

et al. 2024

PeerJ Computer Science

Self Cite

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“…RoBERTa has been utilized in numerous studies. The research conducted by Malik et al (2023) combined utilization of multilingual and translation-based methodologies, as investigated in this study, offers a promising avenue for addressing the intricate task of detecting hope speech across various languages [52]. This approach facilitates the classification of content in diverse linguistic contexts.…”

Section: Robertamentioning

confidence: 96%

Improving Clothing Product Quality and Reducing Waste Based on Consumer Review Using RoBERTa and BERTopic Language Model

Alamsyah,

Girawan

2023

BDCC

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The disposability of clothing has emerged as a critical concern, precipitating waste accumulation due to product quality degradation. Such consequences exert significant pressure on resources and challenge sustainability efforts. In response, this research focuses on empowering clothing companies to elevate product excellence by harnessing consumer feedback. Beyond insights, this research extends to sustainability by providing suggestions on refining product quality by improving material handling, gradually mitigating waste production, and cultivating longevity, therefore decreasing discarded clothes. Managing a vast influx of diverse reviews necessitates sophisticated natural language processing (NLP) techniques. Our study introduces a Robustly optimized BERT Pretraining Approach (RoBERTa) model calibrated for multilabel classification and BERTopic for topic modeling. The model adeptly distills vital themes from consumer reviews, exhibiting astounding accuracy in projecting concerns across various dimensions of clothing quality. NLP’s potential lies in endowing companies with insights into consumer review, augmented by the BERTopic to facilitate immersive exploration of harvested review topics. This research presents a thorough case for integrating machine learning to foster sustainability and waste reduction. The contribution of this research is notable for its integration of RoBERTa and BERTopic in multilabel classification tasks and topic modeling in the fashion industry. The results indicate that the RoBERTa model exhibits remarkable performance, as demonstrated by its macro-averaged F1 score of 0.87 and micro-averaged F1 score of 0.87. Likewise, BERTopic achieves a coherence score of 0.67, meaning the model can form an insightful topic.

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“…By exploiting its robust linguistic comprehension and sophisticated algorithms, RoBERTa effectively categorized the sentiments expressed by customers as positive, negative, or neutral. The integration of RoBERTa enhanced the accuracy and depth of this sentiment analysis, providing valuable insights into the overall sentiment trends across different restaurant branches and customer interactions [43].…”

Section: B Exploring Sentiment Analysis With Roberta and Bertmentioning

confidence: 99%

Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis for Chain Restaurants using Machine Learning

Kawtar,

Fathi,

Assad

et al. 2024

IJACSA

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In recent years, aspect-based sentiment analysis of restaurant business reviews has emerged as a pivotal area of research in natural language processing (NLP), aiming to provide detailed analytical methods benefiting both consumers and industry professionals. This study introduces a novel approach, Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis (HISABSA), which combines lexicon-based methods such as VADER Lexicon, the AFFIN model, and TextBlob with contextual methods. By integrating advanced machine learning (ML) techniques, this hybrid methodology facilitates sentiment analysis, empowering chain restaurants to assess changes in sentiments towards specific aspects of their services across different branches and over time. Leveraging transformer-based models such as RoBERTa and BERT, this approach achieves effective sentiment classification and aspect extraction from text reviews. The results demonstrate the reliability of extracting valid aspects from online reviews of specific branches, offering valuable insights to business owners striving to succeed in competitive markets.

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Multilingual hope speech detection: A Robust framework using transfer learning of fine-tuning RoBERTa model

Cited by 5 publications

References 17 publications

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Improving Clothing Product Quality and Reducing Waste Based on Consumer Review Using RoBERTa and BERTopic Language Model

Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis for Chain Restaurants using Machine Learning

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