The latent learning model to derive semantic relations of words from unstructured text data in social media

Seo, Jiwan; Yoo, Karam; Choi, Seungjin; Kim, Yura Alex; Han, Sangyong

doi:10.1007/s11042-018-6211-2

Cited by 2 publications

(1 citation statement)

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“…A word cloud [60] is a text visualization method for evaluating information presented in the text. It indicates the importance of words by showing the most frequently used words in bigger font in comparison to the least frequently used words.…”

Section: Word Cloudmentioning

confidence: 99%

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19

Kour

Gupta

2022

Neural Process Lett

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COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is finetuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets. KeywordsCOVID-19 • Transfer learning • Tweets • Sentiment analysis • LSTM • BERT Abbreviations BERT Bidirectional encoder representations from transformers LSTM Long short-term memory network RETN Residual encoder transformation network SARS-CoV-2 Severe acute respiratory syndrome -Coronavirus

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Section: Word Cloudmentioning

confidence: 99%