Abstract:
In recent years, the Internet has become a global platform for communicating and disseminating
information. Today, popular social media sites have a huge global reach and audience with many
daily active users who interact and communicate through those social networks far apart from
each other physically. Because there is no limitation that guides the users of the platform such as
Facebook, twitter and YouTube, all users freely express their feelings, attitudes, beliefs and
opinions towards various issues such as politics, social, religious, ethnic, business and etc. As a
result, large volume of opinionated texts is produced on a regular basis; the comments might be
positive or negative. It’s impractical and tedious to manually classify each and every review with
human power into different class such as extreme, anti-extreme and neutral. Classifying the
sentiments is used for the individuals, company as well as the government to make decision and
action on the posts and comments to be removed that contains radical contents that may cause
offence and conflict among the people. In the proposed work we designed and implemented deep
learning-based sentiment analysis model for Afaan Oromo review. Dataset from three domains
which is politics, religious, and ethnic is collected for the model. Totally 2410 reviews which is
labeled with three classes extreme, anti-extreme and neutral labeled with 0,1, and 2 respectively
prepared for the mode. Preprocessing techniques such as stop-word removal, tokenization, lower
case conversion and other preprocessing techniques are used for cleaning the dataset. In the
proposed model sentence level sentiment analysis is used. We implemented convolutional neural
network (CNN) and long short memory (LSTM) with word2vec that classify the reviews into
extreme, anti-extreme and neutral. The proposed model convolutional neural network achieve
accuracy of 80% and long short-term memory achieved performance of 70%. According to our
experiment convolutional neural network is good in classifying the review to the target class than
the long short-term memory.
