‘Opinion-mining’ algorithm summarizes social media sentiments quickly

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Documents that convey an viewpoint abound, especially in the so-known as web 2. era of social media and social networking. Jae-Youthful Chang of the Office of Computer Engineering at Hansung University, in Seoul, South Korea, suggests that there is a will need to find techniques to summarize their contents for a vast array of apps.

Crafting in the Worldwide Journal of Computational Vision and Robotics, he factors out that standard textual content summarization solutions do not perform effectively with a number of documents authored by distinct writers. He has now proposed an algorithm that can determine and extract the representative documents from a large selection of paperwork. Making use of the system could possibly be the first stage toward a new solution to “belief mining,” which could be handy in politics, advertising and marketing, education, and several other spots of human endeavor.

The technique involves detecting the sentiment of the most important—judging—document in a corpus and then ranking the relevance of many others from this central position to allow for a summary of the thoughts expressed to be built. A thriving evidence of principle was carried out on movie reviews. The same method really should operate well with product evaluations and other forms of opinion.



Far more data:
Jae Young Chang. Multi-doc summarisation using attribute distribution assessment, Worldwide Journal of Computational Eyesight and Robotics (2020). DOI: 10.1504/IJCVR.2020.105681

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‘Opinion-mining’ algorithm summarizes social media sentiments quickly (2020, March 13)
retrieved 13 March 2020
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