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