Institution
Yeungnam University
Education•Daegu, South Korea•
About: Yeungnam University is a education organization based out in Daegu, South Korea. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 9885 authors who have published 22075 publications receiving 372798 citations.
Topics: Thin film, Catalysis, Photocatalysis, Control theory, Population
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors performed Covid-19 tweets sentiment analysis using a supervised machine learning approach using a bag-of-words and the term frequency-inverse document frequency.
Abstract: The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.
144 citations
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TL;DR: In this paper, the traditional limit equilibrium and finite element methods are expanded to unsaturated conditions using a generalized effective stress framework, where effective stress is represented by the suction stress characteristic curve (SSCC) and the soil water retention curve (SWRC) have been unified with the same set of hydromechanical parameters.
144 citations
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144 citations
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TL;DR: Through two well-known numerical examples used in other literature, it will be shown the proposed stability criteria achieves the improvements over the existing ones and the effectiveness of the proposed idea.
144 citations
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TL;DR: The present review deals with the growing concern towards cancer therapy, introduction of ChNPs, mode of action and other strategies employed by researchers till date towards cancer treatment and diagnosis ChNPS.
144 citations
Authors
Showing all 9974 results
Name | H-index | Papers | Citations |
---|---|---|---|
Kenneth J. Pienta | 127 | 671 | 64531 |
Hojjat Adeli | 103 | 511 | 30859 |
Ahmad Fauzi Ismail | 93 | 1357 | 40853 |
Herbert C. Brown | 90 | 1357 | 39618 |
Alan J. Wein | 87 | 1164 | 47916 |
Ju H. Park | 83 | 769 | 27512 |
Peter W. Carr | 77 | 517 | 22507 |
J. M. White | 68 | 583 | 18754 |
David H. Sherman | 68 | 386 | 16858 |
Thomas A. Hamilton | 68 | 171 | 15964 |
Ashutosh Sharma | 66 | 570 | 16100 |
Zheng-Guang Wu | 63 | 284 | 12968 |
Moo Hwan Cho | 60 | 195 | 10212 |
Han-Gon Choi | 58 | 421 | 13449 |
Jintae Lee | 56 | 178 | 10393 |