In general, extensive linguistic resources are expensive to build sentiment classification on the less dominant languages (e.g. Amharic). To reduce this problem, we proposed negation han-dling approach and character ngram approach for Sentiment analysis of Amharic face book news comments. We evaluated the usefulness of the combination of Negation Handling (NH) and character level ngram based machine learning models for sentiment classification of Amharic Facebook news comments. We call the combination (i.e. hybrid) of rule based NH and machine learning algorithms (logistic regression and Naive Bayesian) using character ngram based tfidf features for Amharic sentiment classification. The proposed approaches are evaluated by measuring accuracy of individual and their combinations for Amharic text sentiment classification. Amharic negation scope identification and handling is recommended for further researches. We also suggest method to consider character ngram embedding features from corpus of the same domain(e.g. Facebook news comments).
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