A method for sentiment analysis of film reviews based on deep learning and natural language processi

Abstract

A method for sentiment analysis of film reviews based on deep learning and natural language processing is disclosed. The method for analyzing emotions of film reviews by deep learning includes: getting film reviews text data and marking positive and negative emotions in film reviews; preprocessing the film reviews by removing redundant information; vectorizing film reviews text according to the bag-of-words model; splitting the vectorized film reviews into training sets and test sets; setting up the initial deep learning model of film reviews sentiment analysis, which connects and integrates four convolution neural network layers, two pooling layers, and two full connected layers; training the initial deep learning model by training data set to generate the final deep learning model, using the final deep learning model to detect the film reviews test set and output the detection results. The invention can accurately distinguish positive and negative emotions of film reviews, and the deep learning model has a simple structure and a small amount of calculation, thereby improving the speed of emotion analysis of film reviews. a raw review -Remove HTML, review text -n n-leters- letters only lowercase & split into individual ones Join a clean review 4-the words- meaningfulwords * Remove - words together stopwords Figure 1 [sentense] John likes to watch movies. Mary likes too. John also likes to watch football game. mokes 52 to:3 John likes to watchmovies.Marylikestoo. Johnalso likes to watch football game. [1, 1, 1, 1, 0, 1, 1, 1, 0, 0] Figure 2