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