Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text

In previous work [1], it was shown that NeuroFuzzy Models (ANFIS) applied to dialog analysis could determine user intent with reasonable accuracy. Hidden Markov models (HMMs) achieved comparable accuracy, but with a different pattern of errors. A hybrid approach that fused the two methods was more accurate that either alone. This technique has been modified to extract sentiment from the "Rotten Tomatoes" movie review database. The reported systems include HMM only, ANFIS only, and a hybrid of the two. The two single-component systems each perform 82-83% correct results from unedited reviews. The hybrid system is able to improve accuracy by a full percentage point, achieving 84% correct. It is anticipated that when an automatic editing module is inserted, accuracy will improve to a level commensurate with human judgment.