Text data opinion summary mining method based on topic diversity

Abstract

The invention provides a text data opinion summary mining method based on topic diversity. The method comprises the following steps: S1, preprocessing topic text; S2, inputting a topic corpus and a background corpus; S3, extracting topic attributes of the topic corpus; S4, adding emotional polarity to the obtained topic attributes for vectorizing a sentence; S5, taking the obtained topic attributes as evaluation objects, analyzing the emotional polarity of the evaluation objects contained in the sentence with a dynamic word sequence emotional analysis method for multiple evaluation objects, obtaining emotional attribute characteristics contained in the sentence, and performing characteristic vectorizing on the sentence; S6, constructing a diversity objective function with a text sentence characteristic vector obtained in S5. An opinion summary of a topic text can be obtained efficiently and accurately, and the method can be applied to a larger data set application scene.

Description

technical field [0001] The present invention relates to the fields of text summarization and sentiment analysis, and more specifically, relates to a method for generating short opinion summaries with rich user emotional information for massive topic text data of Chinese microblog corpus, and the opinion summaries can accurately cover the text discussed It can be applied to practical application scenarios such as news summaries and commodity review summaries. Background technique [0002] Currently, there are many techniques and methods available for research in the field of opinion summarization. Traditional view summarization models include graph models and ranking models. The representative methods of graph models include Textrank, PageRank, LexRank and other methods. They use sentences as nodes, and a certain relationship between sentences as the weight of edges, and iteratively update and calculate the scores of sentences through the random walk model, so as to realize ...

AI Technical Summary

Problems solved by technology

The disadvantage is that most of the existing models consider using the diversity of all words in the text sentence to ensure that the opinion summary covers the main idea of ​​the text, and the diversity of the words is used to ensure the diversity of the summary, but the diversity of words does not guarantee that the opinion summary includes The subject of the source text, words that are not related to the subject will affect the final opinion summary, and the existing research methods describe the emotional information of the summary through the emotional information of the entire text sentence, and the emotions of many irrelevant text subjects are also taken into account. The two factors lead to the final summary containing a lot of content and emotional information that are not related to the main theme of the text

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