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.
Parsing data files is always a little difficult, since you can’t be sure that your data is formatted properly. I mentioned in earlier posts that I am currently creating a Reader for my training data. Here is how I am doing.
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 am far from creating the best code possible but last week I spent some time writing a half decent Reader for my training data sets. I will write my code in Java, since it’s my most fluent programming language. But first I’ll write some lines about the pipeline.
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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