In recent years, sentiment analysis and emotion classification are two of the abundantly used techniques in the field of Natural Language Processing(NLP). Although sentiment analysis and emotion classification are used commonly applications such as analyzing customer reviews, the popularity of contesting in elections, and comments about various sporting events;, in this study, we have examined their application for epidemic detection. Early outbreak detection is the key to deal with epidemics, however, the traditional ways of outbreak detection are-consuming which inhibits prompt response from the respective departments. media platforms such as Twitter, Facebook, Instagram, etc. allow the to express their thoughts related to different aspects of life, and, serve as a substantial source of information in such situations. The study exploits the bilingual (Urdu and English) data from Twitter and websites related to the dengue epidemic in Pakistan, and sentiment and emotion classification are performed to acquire deep insights from data set for gaining a fair idea related to an epidemic outbreak. Machine and deep learning algorithms have been used to train and implement the for the execution of both tasks. The comparative performance of each has been evaluated using accuracy, precision, recall, and f1-measure.
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Ali et al. (2021) studied this question.