Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis
| Title | Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | J Med Internet Res. |
| Volume | 25 |
| Start Page | e47014 |
| Date Published | 10/2023 |
| Authors | Laison EKE, Ibrahim MH, Boligarla S, Li J, Mahadevan R, Ng A, Muthuramalingam V, Lee WY, Yin Y, Nasri BR |
| Keywords | BERT, Bidirectional Encoder Representations from Transformers, emojis, Lyme disease, machine learning, natural language processing, Twitter |
| Abstract | The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives. |
| URL | https://www.jmir.org/2023//e47014/ |
