A text data opinion summarization mining method that integrates topic attributes and sentiment information

Abstract

The present invention provides a text data viewpoint summary mining method that combines topic attributes and emotional information, including: preprocessing the text corpus of the topic; inputting the topic corpus and the background corpus; extracting the topic attributes of the topic corpus; and obtaining Add emotional polarity to the topic attribute of the sentence and vectorize the sentence; use the obtained topic attribute as the evaluation object to obtain the emotional attribute characteristics contained in the sentence, and use the topic attribute and sentiment analysis method to perform feature vectorization on a sentence; use the obtained topic The attribute set and text sentence feature vector set S construct a three-layer graph structure to cluster all text sentences; select sentences from clusters to form opinion summaries, and select sentences with high scores to form opinion summaries. The invention makes the topic attributes extracted by the method of extracting topic attributes more accurate, and also makes it not only applicable to the field of Chinese microblog, but also applicable to the fields of website news and commodity reviews.

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 and can be applied to practical application scenarios such as news summaries and commodity review analysis. 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 graph models consider using text sentences and topic features to construct graph structures, and describe the emotional information of opinion summaries through the emotional information of the entire text sentence, without integrating the emotional information of topic attributes in the graph structure, there is no Considering that the topic features of different emotions are two subjects with different meanings, resulting in sentences containing different emotional topic attributes being associated

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