Abstract:
It’s a natural human ability to understand emotions and analyze situations. But the
effectiveness with which machines can be trained to demonstrate the same human
phenomenon becomes an important question to be explored. Sentiment Analysis (SA) is also
considered as sentiment classification in which text which is a document, posts, reviews or
tweet is categorized either negative or positive. To know the opinions of people, twitter is
considered a rich source platform.SA is being studied for a long time, but in the Urdu
language, it is a new research area. This thesis uses a deep learning approach for
classifying sentiments in the Urdu Language. Dataset available in this language is not
sufficient. In this research, Urdu Tweets are extracted. And manually labeled as negative or
positive. Convolution Neural Network CNN is used for classification. As CNN does not
directly work on textual data, we use word2vec for converting textual data into vectors. We
train word2vec on Urdu language data. Accuracy of 74 % is achieved by using word2vec
embedding feature with CNN.
