Name
Abstract | ||
|---|---|---|
COVID-19 has been one of the most dominant discussion topics on Twitter since 2019. Users express their opinions representing public sentiment on the topic. This paper presents a sentiment timeline of Twitter users, regarding COVID-19 vaccines. This work raises concerns about the extracted information with regards to sentiment analysis, the dominance of each sentiment and its influential power. During the implementation of the analysis, several datasets were examined for the creation of the model. Various algorithms were employed with Random Forest performing best and therefore selected for training the model, achieving an accuracy of 91.5%. Our findings indicate that the majority of Twitter users are positive regarding COVID-19 vaccines and support WHO’s recommendations. Negative tweets comprising the minority of the tweets, appear to have a higher influential power with their retweet rates, outperforming positive and neutral sentiments. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-08337-2_29 | Artificial Intelligence Applications and Innovations |
Keywords | DocType | ISSN |
Sentiment analysis, Text mining, Social media, Vaccination, COVID-19 | Conference | 1868-4238 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karagkiozidou Makrina | 1 | 0 | 0.34 |
| Koukaras Paraskevas | 2 | 0 | 0.34 |
| Christos Tjortjis | 3 | 173 | 24.40 |