Abstarct: One of the main factors in all text polarity detection methods is the sentimental phrases in that text. Most dictionary-baxsed methods have focused on extracting sentiment words, determining their polarity, and finally assigning the sentimental score to these words. On the other hand, almost all researchers in sentiment analysis emphasize that a text can contain linguistic phenomena that affect text polarity, although they do not contain any sentimental polarity. Negators and intensifiers are two important linguistic phenomena that can increase the accuracy and efficiency of the polarity detection process. Lacking accurate attention to the impact of negators and intensifiers and the position of these phenomena in a sentiment phrase and neglecting the effect of the polarity of sentiment words on these linguistic phenomena are some disadvantages that reduce the efficiency of existing sentiment dictionaries. According to the above details, this study has proposed a new high-coverage semi-supervised sentiment dictionary that provides the ability to calculate senti-n-gram exxpressions made from a combination of sentimental words and several intensifiers and negators. Sentimental word scores were extracted from the manual VADER dictionary. The intensifiers coefficients were extracted automatically using a hybrid fuzzy-statistical method baxsed on the intensifier location and the sentiment word polarity. In the proposed method, four different coefficients are assigned to each intensifier. The simulation results show that compared to the latest methods in this area, the proposed dictionary has at least 9% improvement in standard databaxses. The improvement in the field of document with negative polarity is 49.6%.