A new sentiment polarity recognition model based on linguistic structure of network reviews - Fixed sentiment terms model

Emotional states are part of the information that is conveyed in many forms of network reviews. This paper presents a new sentiment polarity recognition model based on linguistic structure of emotion states-fixed sentiment terms model. The proposed method uses three types of specific collocation pattern to construct the recognition algorithm based on fixed sentiment terms. These feature term sets are gradually updated by relevance feedbacks from the users which based on incremental tf-idf model. Comparison is done between the traditional method and fixed sentiment terms model. All tests showed the proposed method gets a higher efficiency and accuracy rate of the emotion classifier.