Performance Evaluation of Domain Specific Sentiment Dictionary Construction Methods for Opinion Mining
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 frequencybased approaches show better performance than SO PMI.