Performance Evaluation of Domain-Specific Sentiment Dictionary Construction Methods for Opinion Mining

Abstract

Sentiment dictionaries or lexicons are core elements for “bag-of-word” approaches of opinion mining or sentiment analysis. Rather than using general-purpose sentiment dictionaries, domain-specific sentiment lexicons can contribute to improve performance because they can reflect domain specific terms and meanings. This paper presents four domain-specific sentiment dictionary construction methods for opinion mining, and describes performance evaluation results using a practical data set. The comparison subjects of this research include SO-PMI (Semantic Orientation from Pointwise Mutual Information) and three term frequency-based methods with different term polarity measures. To evaluate the performance of four different methods, a movie review data set from a representative Internet movie community site, IMDb (Internet Movie Database) is collected using a web crawling program, and is analyzed using R programs. Based on training data set, domain specific sentiment dictionaries are constructed using four different methods, and are compared their performance of sentiment analysis. The experimental results show that domain-specific sentiment dictionaries are working better than general-purpose dictionaries except one genre, „animation‟. Also, term frequency-based approaches show better performance than SO-PMI.