DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa

Laxmi, Sirigireddy Dhana; Agarwal, Rohit; Sinha, Aman

doi:10.18653/v1/2020.wnut-1.58

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…And RoBERTa outperforms among these three approaches. Reference [ 53 ] used RoBERTa to classify informative tweets related to COVID-19 and their approach showed the best results.…”

Section: Types Of Classification Algorithmmentioning

confidence: 99%

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Qasim

Bangyal

Alqarni

et al. 2022

Journal of Healthcare Engineering

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Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

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“…And RoBERTa outperforms among these three approaches. Reference [ 53 ] used RoBERTa to classify informative tweets related to COVID-19 and their approach showed the best results.…”

Section: Types Of Classification Algorithmmentioning

confidence: 99%

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Qasim

Bangyal

Alqarni

et al. 2022

Journal of Healthcare Engineering

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“…In order to ascertain consumer sentiment about particular brands or products, sentiment analysis is of paramount approach for researchers. Since it is impossible to manually keep up with the huge ow of new information appearing on the Internet, the eld of automatically extracting opinions has signi cantly been evolving in the last decade (Dhana Laxmi et al, 2020;Zhang et al, 2020). SA takes into account not only the positive or negative polarity of words and concepts, but also the syntactical tree of the sentence (Obaidi et al, 2022;Prager, 2006).…”

Section: 2 Sentiment Analysismentioning

confidence: 99%

A bibliometric analysis to explore sentiment analysis in the domain of social media research

Dahish¹,

Miah²

2022

Preprint

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Businesses use sentiment analysis in different ways for generating market or consumer insights to improve their performance. Existing sentiment analysis studies related to social media research have been significant due to the fact of their rapidly growing potential, as this type of analysis of social media content represents immense benefits. It is imperative to explore the need for such analysis to delineate the trend holistically for knowledge landscaping purposes. The study addresses the research gap through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the performance, content, and thematic analysis. We adopt an automated bibliometric study approach using the R-tool- biblioshiny for generating and presenting outcomes. Finding points on the vital usages of sentiment analysis such as innovation, transparency, and improved efficiency. It also highlights the uniqueness of sentiment analysis for synthesizing social media content to examine various aspects such as the knowledge-domain map that detects author collaboration networks as well as journal relationships.

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