Research On Sentiment Analysis Based On The Ensembled Extended Topic Model

With the rapid development of the Internet technology and the popularization of various types of applications in people's daily lives,more and more people are used to publishing opinions on news,events or products on the Internet.Sentiment analysis of the subjective unstructured texts has an important role in the field of public opinion monitoring,electronic commerce and information prediction.Therefore,sentiment analysis is import for both theory and practice.This paper mainly focuses on the following two parts.First of all,the classical LDA topic model is extended with the features of the TF-IDF weighted n-gram linguistic model.The LDA topic model is based on the word bag model,which has less semantic information because of the independent relations between words.The classical LDA topic model is extended with semantic information,which comes fr-om the TF-IDF weighted n-gram features.The extended topic model improves the performance of sentiment analysis by adding semantic information.Secondly,considering the stability and strength of the ensemble learning,the method of ensemble is used based on the extended LDA topic model.The sub-training set of the ensembled extended topic model is divided by the topic of text from text-topic probability distribution.The difference between each base classifier comes from the sub-training set which sampled based on the different topic of each text.The result of the ensembled extended topic model to analyse sentiment is obtained by the simple voting method.Through theoretical derivation and experiments,the result of sentiment analysis is improved by using the method of the ensembled extended topic model.The complexity of the ensembled extended topic model has not increased significantly.