A Unified Framework for Emotional Elements Extraction Based on Finite State Matching Machine

Traditional methods for sentiment analysis mainly focus on the construction of emotional resources based on the review corpus of specific areas, and use phrase matching technologies to build a list of product feature words and opinion words. These methods bring about the disadvantages of inadequate model scalability, low matching precision, and high redundancy. Besides, it is particularly difficult to deal with negative words. In this work, we designed a unified framework based on finite state matching machine to deal with the problems of emotional element extraction. The max-matching principal and negative words processing can be integrated into the framework naturally. In addition, the framework leverages rule-based methods to filter out illegitimate feature-opinion pairs. Compared with traditional methods, the framework achieves high accuracy and scalability in emotional element extraction. Experimental results show that the extracting accuracy is up to 84%, which has increased by 20% comparing with traditional phrase matching techniques.