Exact(8)
When duplicate or confusing topics arose, curators were told to "inject" a more accurate Topic term.
where is the term vector of the user, and is the topic term vector.
In the third stage, semantic similar terms are computed for each topic term generated in previous stage.
With Topic Insights, media partners just enter a topic term, and Facebook returns data about anyone who mentioned a world related to that term.
We then calculate the cosine similarity between the profile term vector and the topic term vector to get the member's inner profile rank.
Also useful: search queries can be automatically saved so users can revisit specific results at any time, and any piece of social content can be referenced historically for any topic, term or link.
Similar(51)
Fig. 9 Mapper and reducer for semantic terms generation from cluster topic terms.
The mapper and reducer for semantic terms generation from cluster topic terms is presented in the Fig. 9.
The mapper and reducer for topic terms generation from document clusters is shown in the Fig. 8.
It is important to note that the topic modelling algorithm returns purely a distribution of topic terms that do not come with a semantic interpretation.
Then the terms are arranged in the descending order of frequency and top N topic terms (including the semantic similar terms) are selected.
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