Method for collecting network public feelings viewpoint

Abstract

The invention discloses a network public feelings collecting method; hot words are extracted from a network forum, related information documents are extracted according to the hot words to form a hot event document set which is related to the hot words; the key sentences in the hot event document set are clustered to obtain a plurality of view topic sentence sets on one hot event; then a feelings word stock with different types is established, feelings marking is carried out on the feelings words of the key sentence in the view topic sentence set; the feelings tendency value of each key sentence is calculated according to the feelings marking; the feelings tendency value of each netizen on the view topic is calculated according to netizen ID mark of the key sentence to obtain the network public feelings about some hot event, thus achieving the collection of the network public feelings. The invention not only can effectively find the public feelings information in the network, but also can timely and accurately extract netizen view points aiming at concrete public feelings, and makes up the defect that the previous public feelings system can only find the public feelings information, but the view can not be analyzed.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps gather data about people's preferences by analyzing their comments or mentions made during an internet forum session with other users who share similar interests. It does this without requiring any specialized knowledge beyond what others are already familiar.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving internet reputation and understanding community dynamics around specific areas like school or workplace meetings where individuals frequently discuss various aspects related to them (such as political belief). Current surveillance methods focused solely on identifying open connections between users who share similar interests but without taking into account any significant differences from those with no interest. There currently exist tools called webcalls that provide detailed feedback about these interactions within an organization. While there exists existing toolboxes specifically designed to gather data based upon user behavior, none provides precise timing and direction indicators for future outbreaks.

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