LOOQME is a service for PR managers which helps to monitor all brand mentions in media and get full pr-campaign analytics. Using our service PR managers can design, optimize and control the goals of their communication strategies in a few clicks, measure campaigns efficiency, and get useful insights.
LOOQME system gathers needed data from media and labels it. Based on this it creates full analytics about the impact of every mention, competitor’s activity and market situation in real time.
LOOQME service provides media monitoring, alerts, analytics.
LOOQME helps PR professionals to save time and resources, improve KPIs, always stay informed and maintain a good reputation in media.
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Based on our record, FuzzyWuzzy seems to be more popular. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: about 2 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago
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