Using SVR and Rules on pharse Valence-Arousal Prediction

• Main Authors: Jia-Nan Hu, 胡佳男
• Other Authors: Guo-Hua Lai
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/8h95mh

碩士 === 元智大學 === 資訊工程學系 === 105 === With the rapid development of the network and the popularity of social networking platforms, more and more users on the network exchange views, share experiences, express their views. Therefore, there are a large amount of emotional information. Affective computing is a hot research direction in the fileid of natural language processing. Affective computing has also become an important direction of sentiment analysis for huge emotional information on network . In the past,most study put the emotions into different categories, this brings many defects. In recent years, there is also a new emotional dimension type analysis method based on the emotional intensity of the emotional word is converted into two successive scores whichi are Valence (the degree of positive or negative) and Arousal (the degree of excitement and calm), and then calculated for emotional words in the text drawn emotional intensity. This paper is based on the dimension type of sentiment analysis method, a dictionary that contains 1653 Chinese dictionary Valence-Arousal emotional words as the research base. first introduced the word vector to SVR training model to predict the emotional word’s Valence and Arouse, and emotional words for modification of the defect structure of the text emotional intensity calculation make improvements in the text. Emotional analysis dimension is based on the previous first identify the emotional words and modified structure of the sentence, and put modifier structure into some categories and give weight manner calculated after modification of emotional intensity in the modified structure, herein for modifying the structure, intended for different modified structure given different modification rules, complete the calculation of the final text of emotional intensity.