SAFS3 Algorithm: Frequency Statistic and Semantic Similarity Based Semantic Classification Use Case 
Advances in ICT for Emerging Regions (ICTer), 2015 Fifteenth International Conference on
Sentiment analysis on movie reviews is a topic of interest for artists and businessmen alike for the purpose of gauging the reception of an artwork or to understand the trends in the market for the benefit of future productions. In this study we introduce an algorithm (SAFS3) to classify documents into multiple classes. This paper then evaluates the SAFS3 algorithm through the use case of analysing a set of reviews from Rotten Tomatoes. Thenovel algorithm results in an accuracy of 53.6\%. SAFS3 algorithm outperforms the benchmark for this context as well as the set of generic machine learning algorithms commonly used for tasks of this nature.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Ontologies | Sentiment Analysis | Semantic Similarity | TF-IDF | Classification |