Research On Sentiment Classification Of Portal Reviews Based On GDBN And XGBOOST

In the era of widespread Web2.0 technology,Internet information technology continues to develop and mature,major social platforms and e-commerce platforms are rapidly emerging,and network users have more initiative and voice.As the scale of Chinese netizens continues to expand,portals have become the main information dissemination channel.More and more online users express their feelings and share their opinions on the portal,which makes the commentary information on the portal grow,resulting in a large number of irregular comment text data,and these data are continuously updated in real time.How to extract the huge commercial value and the value of public opinion behind the massive and irregular comment text data in real time and classify the sentiment orientation through the model has become a key research content in the field of natural language processing.This topic proposes a portal commentary sentiment classification model based on GDBN and XGBoost for precise and efficient consideration.The model uses the Genetic Deep Belief network to extract the deep features of the Chinese comment text data collected from the portal,and then uses the cost-sensitive learning XGBoost algorithm to classify the sentiment orientation.The commentary sentiment classification model of this paper mainly includes two parts: feature extraction and emotion classification.In the first part,the genetic algorithm is used to solve the problem that the number of hidden layer neurons in the traditional deep belief network is difficult to select,and the flexibility of the classification model,the accuracy of collecting the deep features of the comment text and the running speed of the model are improved.In the second part,combined with the advantages of cost-sensitive learning method and XGBoost algorithm,the cost-sensitive learning XGBoost algorithm is used to classify the emotional tendency of the deep-seated features of the comment text collected by the model.Finally,this article uses the Ctrip hotel user reviews collected by Professor Tan Songbo from the Chinese Academy of Sciences and the mobile phone part comments on the Jingdong e-commerce platform based on the Scrapy framework for crawler collection as the experimental data set of the model.Then the test sentiment classification model proposed in this paper is tested on the experimental data set.The test results show that the proposed model has higher fittingand generalization ability in text sentiment classification,and can accurately,efficiently and quickly complete the classification of comment emotions on the portal.