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…
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…
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.
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.
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.
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