Sentiment Mining Model for Opinionated Amharic Texts

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Addis Ababa University


Opinions are so important that whenever we need to make a decision, we want to hear other’s opinions. This is not only true for individuals but also for organizations. Due to the rapid growth of opinionated documents, reviews and posts on the Web, the need for finding relevant sources, extract related sentences with opinions, summarize them and organize them to useful form is becoming very high. Sentiment mining can play an important role in satisfying these needs. The process of sentiment mining involves categorizing an opinionated document into predefined categories such as positive, negative or neutral based on the sentiment terms that appear within the opinionated document. In this research work, a sentiment mining model is proposed for determining the sentiments expressed in an opinionated Amharic texts or reviews. The polarity classification or semantic orientation of the opinionated texts can be positive, negative or neutral. The system designed based on the proposed model detects positive and negative sentiment terms including contextual valence shifters such as negations and assigns an initial polarity weight to all detected sentiment terms in order to determine the polarity classification of the opinionated text. The lexica of Amharic sentiment terms are used to identify and assign initial polarity value to the sentiment terms detected. A prototype system is developed to validate the proposed model and the algorithms designed. Tests on the prototype are done using movie and newspaper reviews where the result obtained with these test data is very much encouraging. Keywords: opinions, sentiments, sentiment mining from opinionated Amharic texts, polarity classification from opinionated Amharic texts, sentiment lexicon, opinionated Amharic text



Opinions, Sentiments, Sentiment Mining from Opinionated Amharic Texts, Polarity Classification from Opinionated Amharic Texts, Sentiment Lexicon, Opinionated Amharic Text