Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model
Abstract India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-02-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-05974-6 |
| _version_ | 1818500853299937280 |
|---|---|
| author | Vaibhav Kumar |
| author_facet | Vaibhav Kumar |
| author_sort | Vaibhav Kumar |
| collection | DOAJ |
| description | Abstract India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc. |
| first_indexed | 2024-12-10T20:48:18Z |
| format | Article |
| id | doaj.art-889c740e34a7449a8d53eb4caabcf763 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-10T20:48:18Z |
| publishDate | 2022-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-889c740e34a7449a8d53eb4caabcf7632022-12-22T01:34:10ZengNature PortfolioScientific Reports2045-23222022-02-0112111410.1038/s41598-022-05974-6Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning modelVaibhav Kumar0Data Science and Engineering, Indian Institute of Science Education and ResearchAbstract India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.https://doi.org/10.1038/s41598-022-05974-6 |
| spellingShingle | Vaibhav Kumar Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model Scientific Reports |
| title | Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model |
| title_full | Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model |
| title_fullStr | Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model |
| title_full_unstemmed | Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model |
| title_short | Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model |
| title_sort | spatiotemporal sentiment variation analysis of geotagged covid 19 tweets from india using a hybrid deep learning model |
| url | https://doi.org/10.1038/s41598-022-05974-6 |
| work_keys_str_mv | AT vaibhavkumar spatiotemporalsentimentvariationanalysisofgeotaggedcovid19tweetsfromindiausingahybriddeeplearningmodel |