Research on text sentiment analysis based on neural network
As a popular research direction in the field of natural language processing in recent years,sentiment classification aims to identify the emotional tendency in text,such as positive,negative or neutral,etc.,and it is of great significance for research-ers and governments to mine and analyze the emotional polarity of a large amount of text data such as social media,news,comments and user feedback.Traditional sentiment classification algorithms usually use statistical-based feature extraction methods,such as bag-of-words models,combined with machine learning algorithms,such as support vector machines(SVMs)and naïve Bayes classi-fiers.Based on the research of neural network,the text sentiment analysis is realized,and the sentiment analysis model is estab-lished after the text data set is preprocessed,use the Keras framework to build a recurrent neural network to identify emotional ten-dencies,after defining the relevant functions,model training is performed,and use a series of methods to test the model perfor-mance.Compared with traditional machine learning algorithms,the accuracy and efficiency of sentiment analysis are improved.
NETL
NSTL
万方数据
