Opinions are so important that whenever we need to make a decision, we want to hear otherâ€™srnopinions. This is not only true for individuals but also for organizations. Due to the rapidrngrowth of opinionated documents, reviews and posts on the Web, the need for finding relevantrnsources, extract related sentences with opinions, summarize them and organize them to usefulrnform is becoming very high. Sentiment mining can play an important role in satisfying thesernneeds. The process of sentiment mining involves categorizing an opinionated document intornpredefined categories such as positive, negative or neutral based on the sentiment terms thatrnappear within the opinionated document. In this research work, a sentiment mining model isrnproposed for determining the sentiments expressed in an opinionated Amharic texts or reviews.rnThe polarity classification or semantic orientation of the opinionated texts can be positive,rnnegative or neutral. The system designed based on the proposed model detects positive andrnnegative sentiment terms including contextual valence shifters such as negations and assigns anrninitial polarity weight to all detected sentiment terms in order to determine the polarityrnclassification of the opinionated text. The lexica of Amharic sentiment terms are used tornidentify and assign initial polarity value to the sentiment terms detected. A prototype system isrndeveloped to validate the proposed model and the algorithms designed. Tests on the prototypernare done using movie and newspaper reviews where the result obtained with these test data isrnvery much encouraging.rnKeywords: opinions, sentiments, sentiment mining from opinionated Amharic texts, polarityrnclassification from opinionated Amharic texts, sentiment lexicon, opinionated Amharic text