Prediction of News readers’ Emotion by N-gram

• Main Authors: Yu-Hsinh Shen, 沈育信
• Other Authors: 張昭憲
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/76863313973440307168

碩士 === 淡江大學 === 資訊管理學系碩士在職專班 === 103 === With the rise of community networks, people began to get used to show their opinion and comment. Network users leaving a large number of publicly available data by their activity. We can extract data to useful and precious information by analysis data carefully to understanding the requirements and preferences of people. Due to highly practicable of emotion analysis, filed , academic and government join the research of public opinion mining. This study will focus on prediction of news readers’ emotion. Government or companies can make decision by referring to emotion of news readers. Collecting large internet news long time and make word segmentation by N-gram on every news. Statistic frequency of key word and create emotion model by news readers’ emotion voting. When predict readers’ emotion of news, this study try to use three method to improve accuracy rate. This study collect internet news from December 8 2013 to November 12 2014, total 193,489 news. This study present high accuracy in some specific category of news. In this study, accuracy rate will improve apparently with news collection time. When grave news occurred, postpone the model timestamp will get better accuracy rate.