Abstract
MicroBlogs have become a popular and important communication platform for Internet users. Many people frequently use the platform to share opinions about different topics. Although a few research papers have employed the use of MicroBlogs for sentiment analysis, the focus has been limited to classifying user sentiment into positive and negative classes. In this paper, we introduce an approach to identify the sentiment strength of words in MicroBlogs. Given a sentiment word, we extract the corresponding strength attributes from MicroBlogs: repeated exclamations, emoticons, repeated words, and repeated characters. By integrating these attributes with genetic algorithms, comparable word strength is provided. The experimental results demonstrate that the proposed approach satisfies the expectations of users and effectively provides comparable metrics on the sentiment strength of words.