Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model
The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/80 |
| _version_ | 1797497628536078336 |
|---|---|
| author | Siva Kumar Pathuri N. Anbazhagan Gyanendra Prasad Joshi Jinsang You |
| author_facet | Siva Kumar Pathuri N. Anbazhagan Gyanendra Prasad Joshi Jinsang You |
| author_sort | Siva Kumar Pathuri |
| collection | DOAJ |
| description | The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree. The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU). |
| first_indexed | 2024-03-10T03:21:58Z |
| format | Article |
| id | doaj.art-4abd93c5bbf144e5b42a87985fe53f56 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:58Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4abd93c5bbf144e5b42a87985fe53f562023-11-23T12:16:32ZengMDPI AGSensors1424-82202021-12-012218010.3390/s22010080Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification ModelSiva Kumar Pathuri0N. Anbazhagan1Gyanendra Prasad Joshi2Jinsang You3Department of CSE, KLEF, Vaddeswaram, Guntur District, Guntur 522502, Andhra Pradesh, IndiaDepartment of Mathematics, Alagappa University, Karaikudi 630003, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, KoreaSeculayer Company, Ltd., Seoul 04784, KoreaThe COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree. The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).https://www.mdpi.com/1424-8220/22/1/80GPUreviewsSADBMparallel processingCUDA |
| spellingShingle | Siva Kumar Pathuri N. Anbazhagan Gyanendra Prasad Joshi Jinsang You Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model Sensors GPU reviews SADBM parallel processing CUDA |
| title | Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model |
| title_full | Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model |
| title_fullStr | Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model |
| title_full_unstemmed | Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model |
| title_short | Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model |
| title_sort | feature based sentimental analysis on public attention towards covid 19 using cuda sadbm classification model |
| topic | GPU reviews SADBM parallel processing CUDA |
| url | https://www.mdpi.com/1424-8220/22/1/80 |
| work_keys_str_mv | AT sivakumarpathuri featurebasedsentimentalanalysisonpublicattentiontowardscovid19usingcudasadbmclassificationmodel AT nanbazhagan featurebasedsentimentalanalysisonpublicattentiontowardscovid19usingcudasadbmclassificationmodel AT gyanendraprasadjoshi featurebasedsentimentalanalysisonpublicattentiontowardscovid19usingcudasadbmclassificationmodel AT jinsangyou featurebasedsentimentalanalysisonpublicattentiontowardscovid19usingcudasadbmclassificationmodel |