Abstract:
Sentiment analysis, also known as emotion analysis, has recently become one of the
growing areas of research related to natural language processing and machine learning.
It is a method to determine emotional feedback of people towards an event. Much
sentiment about specific topics are available online; it allows several parties such as
customers, companies and governments to explore these opinions. An opinion may be
positive, negative or neutral depends on individual’s judgment or evaluation towards a
topic. The analysis of natural language text for the identification of subjectivity and
sentiment has been well studied in terms of the English language, Japanese and others.
Conversely, the work that has been carried out in terms of Afaan Oromoo remains in its
infancy; thus, more cooperation is required between research communities in order to
offer a mature sentiment analysis system for Afaan Oromoo.
This thesis addresses the problem of sentiments classification of Afaan Oromoo language
reviews on Facebook social media and provides the rationale behind the proposed
methods to enhance the performance of sentiment analysis in the Afaan Oromoo
language. The first step is to increase the resources that help in the analysis process; the
most important part of this task is to have annotated sentiment corpora. We also
describes the work undertaken by the author to enrich sentiment analysis in Afaan
Oromoo by building a new Afaan Oromoo Sentiment Corpus. The data is labeled not only
with two polarities, but the neutral sentiment is also used during the annotation process.
The second step includes apply preprocessing on textual data makes ready for learning
features and classifying Afaan Oromoo text into different polarity.
Generally the model is generated by a neural network variance called Convolutional
Neural Network because it have shown great promise in the task of sentiment
classification .The classifier model obtains a classification accuracy of 79.99%, which is
encourage the researchers to continue in this direction of research.
