Movie comment sentiment analysis method based on document vector

Abstract

The invention discloses a movie comment sentiment analysis method based on document vectors. The method comprises the following steps: firstly, carrying out data cleaning and preprocessing on movie comments to construct an emotion analysis data set; and training an improved document vector model by using comments of the movie and scores corresponding to the comments; inputting the movie comments into an improved document vector model to obtain word vectors and document vectors of the movie comments, averaging the word vectors corresponding to the movie comments, and splicing the word vectors with the document vectors to generate feature vectors; and finally, performing emotion classification on the movie comments by using the feature vectors generated based on the movie comments and the score training classification model corresponding to the movie comments. By using the improved document vector generation method, the movie comment sentiment classification accuracy is improved.

Description

technical field [0001] The invention belongs to the field of text classification, and in particular relates to a method for sentiment analysis of movie reviews based on document vectors. Background technique [0002] Movie reviews are comments and opinions on movies that users publish after watching movies. On the one hand, movie evaluations are an important basis for users to choose movies, and users usually know the characteristics, advantages and disadvantages of movies by checking movie evaluations. On the other hand, the producers hope to understand the possible problems of the film and the needs of users through the user's evaluation. Producers can improve the film and improve the quality of the film by analyzing the user's evaluation. Sentiment analysis, as an important part of user evaluation analysis, can classify user comments according to emotional polarity, and can count the ratio of positive and negative emotions in the film to have a more intuitive understand...

AI Technical Summary

Problems solved by technology

However, the document vector obtained by this method using the weighted average of word vectors cannot consider the order of words in the comments, so it will affect the performance of sentiment classification

On the other hand, this type of method uses an unsupervised way to train word vectors, so the trained word vectors can only represent the semantic and grammatical information of words, and cannot represent information related to emotions, which will also affect the performance of sentiment classification.

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