Abstract:
An enormous amount of information is produced daily on the internet about different prod ucts and objects. People like to express their feelings, thoughts in their native language on
different social sites. This bulk data needs to be interpreted. So, Sentiment Analysis (SA)
is required which extracts people’s opinions, feelings, and thoughts. However, it is a highly
domain-dependent task. Due to this, the issue of domain-transfer arises. If a classifier is tested
with any different domain, other than on which it is trained, its performance is affected. Many
tasks and frameworks are created in mostly English and western languages. Tasks intended
for the English language cannot be applied for other languages, hence there is a need to work
on different dialects. In this research, we performed a cross-domain sentiment Analysis on
data set of Urdu language comprising of 9000 sentences from sports tweets in which there
are two domains (Hockey and Cricket).
Furthermore, preparing corpus for specific domains in the Urdu language we applied ma chine learning and deep learning approach. After this, we evaluated results using standard
evaluation measures and a confusion matrix, Gated Recurrent Units (GRU) gives the highest
accuracy of (77%).
