Femonoe can be used to create searches in social networks around a subject. Currently Facebook, Twitter, LinkedIn, YouTube, Google news, Bing News are supported. A search is formed using specific keywords (both positive and negative) and retrieves content that can be sorted based on a combination of keyword relevance, popularity and retrieval date. The output can be embedded on any web site. The idea is to complement your existing content with additional material/resources from social media. The first field where Femonoe was applied is elearning. In elearning a tutor may create a course that can be taught for a number of years as is. However, Femonoe can be used to enrich this course by showing students always the latest discussions and news around the course’s topic keeping it always up to date. The same principle can be applied in many other fields like in blogs, news websites etc.
Femonoe is designed to manage big data sets and follows a distributed approach to achieve fast data processing, indexing and searching. Femonoe’s architecture ensures scalability and high performance.
The secret sauce of Femonoe lies on its relevance ranking algorithm, the module responsible for searching among millions of social media discussions to bring you the latest and most relevant resources about your topic of interest.
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