Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets
The novel coronavirus disease (COVID-19) has dramatically affected people’s daily lives worldwide. More specifically, since there is still insufficient access to vaccines and no straightforward, reliable treatment for COVID-19, every country has taken the appropriate precautions (such as physical se...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/6/2/65 |
| _version_ | 1797489959461978112 |
|---|---|
| author | Nilufa Yeasmin Nosin Ibna Mahbub Mrinal Kanti Baowaly Bikash Chandra Singh Zulfikar Alom Zeyar Aung Mohammad Abdul Azim |
| author_facet | Nilufa Yeasmin Nosin Ibna Mahbub Mrinal Kanti Baowaly Bikash Chandra Singh Zulfikar Alom Zeyar Aung Mohammad Abdul Azim |
| author_sort | Nilufa Yeasmin |
| collection | DOAJ |
| description | The novel coronavirus disease (COVID-19) has dramatically affected people’s daily lives worldwide. More specifically, since there is still insufficient access to vaccines and no straightforward, reliable treatment for COVID-19, every country has taken the appropriate precautions (such as physical separation, masking, and lockdown) to combat this extremely infectious disease. As a result, people invest much time on online social networking platforms (e.g., Facebook, Reddit, LinkedIn, and Twitter) and express their feelings and thoughts regarding COVID-19. Twitter is a popular social networking platform, and it enables anyone to use tweets. This research used Twitter datasets to explore user sentiment from the COVID-19 perspective. We used a dataset of COVID-19 Twitter posts from nine states in the United States for fifteen days (from 1 April 2020, to 15 April 2020) to analyze user sentiment. We focus on exploiting machine learning (ML), and deep learning (DL) approaches to classify user sentiments regarding COVID-19. First, we labeled the dataset into three groups based on the sentiment values, namely positive, negative, and neutral, to train some popular ML algorithms and DL models to predict the user concern label on COVID-19. Additionally, we have compared traditional bag-of-words and term frequency-inverse document frequency (TF-IDF) for representing the text to numeric vectors in ML techniques. Furthermore, we have contrasted the encoding methodology and various word embedding schemes, such as the word to vector (Word2Vec) and global vectors for word representation (GloVe) versions, with three sets of dimensions (100, 200, and 300) for representing the text to numeric vectors for DL approaches. Finally, we compared COVID-19 infection cases and COVID-19-related tweets during the COVID-19 pandemic. |
| first_indexed | 2024-03-10T00:25:04Z |
| format | Article |
| id | doaj.art-c45f44f653984b8583e88dbda959ef66 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T00:25:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-c45f44f653984b8583e88dbda959ef662023-11-23T15:36:32ZengMDPI AGBig Data and Cognitive Computing2504-22892022-06-01626510.3390/bdcc6020065Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using TweetsNilufa Yeasmin0Nosin Ibna Mahbub1Mrinal Kanti Baowaly2Bikash Chandra Singh3Zulfikar Alom4Zeyar Aung5Mohammad Abdul Azim6Department of Information and Communication Technology, Islamic University, Kushtia 7003, BangladeshDepartment of Information and Communication Technology, Islamic University, Kushtia 7003, BangladeshDepartment of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, BangladeshDepartment of Information and Communication Technology, Islamic University, Kushtia 7003, BangladeshDepartment of Computer Science, Asian University for Women (AUW), Chattogram 4000, BangladeshDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, United Arab EmiratesDepartment of Computer Science, Asian University for Women (AUW), Chattogram 4000, BangladeshThe novel coronavirus disease (COVID-19) has dramatically affected people’s daily lives worldwide. More specifically, since there is still insufficient access to vaccines and no straightforward, reliable treatment for COVID-19, every country has taken the appropriate precautions (such as physical separation, masking, and lockdown) to combat this extremely infectious disease. As a result, people invest much time on online social networking platforms (e.g., Facebook, Reddit, LinkedIn, and Twitter) and express their feelings and thoughts regarding COVID-19. Twitter is a popular social networking platform, and it enables anyone to use tweets. This research used Twitter datasets to explore user sentiment from the COVID-19 perspective. We used a dataset of COVID-19 Twitter posts from nine states in the United States for fifteen days (from 1 April 2020, to 15 April 2020) to analyze user sentiment. We focus on exploiting machine learning (ML), and deep learning (DL) approaches to classify user sentiments regarding COVID-19. First, we labeled the dataset into three groups based on the sentiment values, namely positive, negative, and neutral, to train some popular ML algorithms and DL models to predict the user concern label on COVID-19. Additionally, we have compared traditional bag-of-words and term frequency-inverse document frequency (TF-IDF) for representing the text to numeric vectors in ML techniques. Furthermore, we have contrasted the encoding methodology and various word embedding schemes, such as the word to vector (Word2Vec) and global vectors for word representation (GloVe) versions, with three sets of dimensions (100, 200, and 300) for representing the text to numeric vectors for DL approaches. Finally, we compared COVID-19 infection cases and COVID-19-related tweets during the COVID-19 pandemic.https://www.mdpi.com/2504-2289/6/2/65COVID-19tweetssentiment analysismachine learningneural networknatural language processing |
| spellingShingle | Nilufa Yeasmin Nosin Ibna Mahbub Mrinal Kanti Baowaly Bikash Chandra Singh Zulfikar Alom Zeyar Aung Mohammad Abdul Azim Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets Big Data and Cognitive Computing COVID-19 tweets sentiment analysis machine learning neural network natural language processing |
| title | Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets |
| title_full | Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets |
| title_fullStr | Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets |
| title_full_unstemmed | Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets |
| title_short | Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets |
| title_sort | analysis and prediction of user sentiment on covid 19 pandemic using tweets |
| topic | COVID-19 tweets sentiment analysis machine learning neural network natural language processing |
| url | https://www.mdpi.com/2504-2289/6/2/65 |
| work_keys_str_mv | AT nilufayeasmin analysisandpredictionofusersentimentoncovid19pandemicusingtweets AT nosinibnamahbub analysisandpredictionofusersentimentoncovid19pandemicusingtweets AT mrinalkantibaowaly analysisandpredictionofusersentimentoncovid19pandemicusingtweets AT bikashchandrasingh analysisandpredictionofusersentimentoncovid19pandemicusingtweets AT zulfikaralom analysisandpredictionofusersentimentoncovid19pandemicusingtweets AT zeyaraung analysisandpredictionofusersentimentoncovid19pandemicusingtweets AT mohammadabdulazim analysisandpredictionofusersentimentoncovid19pandemicusingtweets |