Good mood and bad mood concept with blocks and smiles in a hands
The technique offers a new approach to opinion-mining, which usually requires a team of dedicated surveyors and participants.
Understanding public
opinion is a requirement for almost all organizations in the internet age, as
campaigns, adverts, and products are launched to fit with current trends.
To meet this demand, a team of researchers at Hansung University in South Korea have developed an algorithm capable of summarizing online sentiments.
SEE ALSO: Machine Learning Algorithms Help Couples Conceive
Published in the
International Journal of Computational Vision and Robotics, the algorithm works
by identifying and extracting representative posts, articles, and stories from
a large swarth of content on a singular topic.
It is able to identify
clearly the most important content, through activity and other metrics, and
build a summarization from that central point. This could be a new approach to
opinion-mining, which usually requires a team of dedicated surveyors and participants.
In the paper, Professor Jae-Young Chang provides proof of the algorithm’s capability, testing sentiment on popular movie reviews. By identifying the most important information online, the algorithm was able to come up with an accurate summary of online sentiment towards a movie.
David is a technology writer with several years experience covering all aspects of IoT, from technology to networks to security.
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