Mentioned in my blog post of last week, I want to get into the different features that I see as very relevant to detect sentiment in tweets. Some of them are easy to detect, some aren’t. Let’s get an overview on Negation first…
A Data Science consultant working at Sopra Steria. He occasionally blogs about data and related topics here and is the host of the Dortmund Data Science Meetup.
I’ve refined and partially overhauled my algorithms to analyze sentiment in Tweets over the last weeks with some notable results. Here is what I came up with so far. I am starting to feel like I’m doing science instead of the tedious tasks I did over my previous semesters.
A Data Science consultant working at Sopra Steria. He occasionally blogs about data and related topics here and is the host of the Dortmund Data Science Meetup.
Reading and interpreting sentences with an algorithm instead by yourself is tough. Reading tweets is worse, so much worse. Let me tell you about some concepts I came up with.
A Data Science consultant working at Sopra Steria. He occasionally blogs about data and related topics here and is the host of the Dortmund Data Science Meetup.
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