Therefore, in this study, we focused on investigating Convolutional Neural Network, Long Short Term Memory and Multinomial Naïve Bayes machine learning approaches for the development of sentiment analysis of Afaan Oromoo social media content such as Facebook posts comments. To the best of the researcher’s knowledge, there is no machine learning approach done for Afaan Oromoo Sentiment analysis to identify the opinion of the people on social media content.

In general, sentiment analysis is the process of automatically identifying and categorizing opinions in order to determine whether the writer's attitude towards a particular entity is positive or negative. Therefore, sentiment analysis has increasingly become a major area of research interest in the ﬁeld of Natural Language Processing and Test Mining. Nevertheless, many organizations and individuals are highly interested to know what other peoples are thinking or feeling about their services and products. The major problem with sentiment analysis of social media posts is that it is extremely vast, fragmented, unorganized and unstructured. This study focuses on sentiment analysis of social media content because automatically identifying and classifying opinions from social media posts can provide significant economic values and social benefits.

The rapid development and popularity of social media and social networks provide people with unprecedented opportunities to express and share their thoughts, views, opinions and feelings about almost anything through their personal webpages and blogs or using social network sites like Facebook, Twitter, and Blogger.

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