Clustering method for topic views based on sentence similarity

Abstract

The invention discloses a clustering method for topic views based on sentence similarity. The clustering method can be used for clustering main views about a certain topic in internet. The clustering method comprises the following steps: firstly, constructing a view lexicon aiming at a topic by utilizing a human-computer cooperation mode; secondly, extracting all view sentences of the topic and performing the view clustering by using the similarity of the view sentences; finally, selecting a representative view sentence for each view class according to the average similarity of the sentences. The clustering method disclosed by the invention has the advantages that a clustering result can be more diversified and refined; a user is enabled to learn the views and details of various parties of the topic more clearly; fuzziness and one-sidedness of view clustering and description are effectively avoided.

Description

technical field [0001] The invention relates to a topic viewpoint clustering method based on sentence similarity, which can be used to realize viewpoint clustering and viewpoint mining of Internet hot topics, and belongs to the technical field of computer networks. Background technique [0002] With the rapid development of the mobile Internet, the content and information on the Internet are complicated, and the characteristics of diverse viewpoints are obvious. In order to increase the in-depth understanding of Internet content topics and avoid being misled by one-sided information, people increasingly need to obtain other people's opinions on a topic from a large amount of Internet information, and help themselves to do better by comparing different opinions. make more rational decisions. For example, when shopping electronically, people often need to judge whether the product is worth buying according to the tendency of product reviews. Opinion clustering is the main me...

AI Technical Summary

Problems solved by technology

Therefore, the classification granularity is relatively rough, and users generally can only understand the polarity of each category, but cannot understand the arguments, arguments and demonstration process of the category

Moreover, for topics with a large number of opinions, or topics where it is difficult to simply use positive and negative to describe opinions, the effect of traditional opinion clustering methods has great limitations, and it is difficult to meet the needs of users

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