Comparison of Text Sentiment Analysis based on Machine Learning

Sentiment analysis is a technology with great practical value, it can solve the phenomenon of network comment information disorderly to a certain extent, and accurate positioning of user information required. Currently for Chinese sentiment analysis research is relatively small, including a variety of supervised learning method of classification result and the text feature representation methods and feature selection mechanism and other factors impact on the classification performance is an urgent problem. In this paper, we taken the verb, adjectives and adverbs as text features, used TF-IDF to calculate weight of words. Then we adopted the SVM and ELM with kernels to analyze the text emotion tendentiousness. The experimental results show that ELM with kernels can be obtained a better classification result in a relatively short period of time than SVM.