But how robust is RoBERTa actually?: A Benchmark of SOTA Transformer Networks for Sexual Harassment Detection on Twitter

Basu, Priyam; Roy, Tiasa Singha; Singhal, Ashima

doi:10.1109/i-smac52330.2021.9640861

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…However, RoBERTa also performs exceptionally well in sentiment analysis, thanks to its deeper language representations [39]. Furthermore, some researchers have suggested that RoBERTa exhibits advantages over other state-of-the-art Transformer architectures in tasks related to text classification and detection [40,41]. Subsequently, we define two fully connected layers, each comprising a linear layer and a ReLU activation function.…”

Section: Two-level Text Feature Extraction Modulementioning

confidence: 99%

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

Wang,

Zheng,

Yao

et al. 2023

Entropy

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In the rapidly evolving information era, the dissemination of information has become swifter and more extensive. Fake news, in particular, spreads more rapidly and is produced at a lower cost compared to genuine news. While researchers have developed various methods for the automated detection of fake news, challenges such as the presence of multimodal information in news articles or insufficient multimodal data have hindered their detection efficacy. To address these challenges, we introduce a novel multimodal fusion model (TLFND) based on a three-level feature matching distance approach for fake news detection. TLFND comprises four core components: a two-level text feature extraction module, an image extraction and fusion module, a three-level feature matching score module, and a multimodal integrated recognition module. This model seamlessly combines two levels of text information (headline and body) and image data (multi-image fusion) within news articles. Notably, we introduce the Chebyshev distance metric for the first time to calculate matching scores among these three modalities. Additionally, we design an adaptive evolutionary algorithm for computing the loss functions of the four model components. Our comprehensive experiments on three real-world publicly available datasets validate the effectiveness of our proposed model, with remarkable improvements demonstrated across all four evaluation metrics for the PolitiFact, GossipCop, and Twitter datasets, resulting in an F1 score increase of 6.6%, 2.9%, and 2.3%, respectively.

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Section: Two-level Text Feature Extraction Modulementioning

confidence: 99%

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

Wang,

Zheng,

Yao

et al. 2023

Entropy

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Sentiment analysis to support business decision-making. A bibliometric study

Aguilar-Moreno,

Palos-Sanchez,

Pozo-Barajas

2024

MATH

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<abstract> <p>Customer feedback on online platforms is an unstructured database of growing importance for organizations, which, together with the rise of Natural Language Processing algorithms, is increasingly present when making decisions. In this paper, a bibliometric analysis is carried out with the intention of understanding the prevailing state of research about the adoption of sentiment analysis methods in organizations when making decisions. It is also a goal to comprehend which business sectors, and areas within the company, they are most applied, and to identify what future challenges that in this area may arise, as well as the main topics, authors, articles, countries, and universities most influential in the scientific literature. To this end, a total of 101 articles have been gathered from the Scopus and Clarivate Analytics Web of Science (WoS) databases, of which 85 were selected for analysis using the Bibliometrix tool. This study highlights the growing popularity of sentiment analysis methods combined with Multicriteria Decision Making and predictive algorithms. Twitter and Amazon are commonly used data sources, with applications across multiple sectors (supply chain, financial, etc.). Sentiment analysis enhances decision-making and promotes customer-centric approaches.</p> </abstract>

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But how robust is RoBERTa actually?: A Benchmark of SOTA Transformer Networks for Sexual Harassment Detection on Twitter

Cited by 2 publications

References 11 publications

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

Sentiment analysis to support business decision-making. A bibliometric study

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