<![CDATA[The film reviews contain an opinion from a reviewer to describe a movie. Assessment of the content from the film review can be called by sentiment analysis. Sentiment analysis on movie review is divided into 2 parts, which are positive review and negative review. Grouping of sentiment analysis results can be simplified by the k-nearest neighbor classification method where this method will look for documents that have similarity between one to another document. In general, the movie review data contains very long content required by feature selection or pruning feature to reduce dimensions during classification process. In this case, the method of information gain is used to reduce many features during the classification process. This method will predict the presence or absence of term in a document so the term that frequently appear has low information gain value, however for the term that rarely appear or only appear in one category has high information gain value. The term with high information gain value will be able to be used for classification process. The result for using all of term for classification is 92% accuracy where the accuracy value is better than the feature selection due to the elimination of term having low information gain value.]]>
Copyrights © 2018
