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I would like to do sentiment analysis on Tweets. The algorithm should serve as a background check if we want to hire someone, but it can also give us a general feeling for customers. Say you launch a product and want to know how customers feel about it. In the end, I would like to have a score, e.g., Person X has a score of 100, meaning her background is without any scandals.

I thought about three models that might solve this problem:

Naive Bayes Support Vector Machine Rule-Based Algorithm What are the theoretical pros and cons to each model? Why might one of these be better for this type of problem? I'm new to machine learning, so what I'd like to understand is why one might do better.

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  • $\begingroup$ Do you have labels for the tweets (something like positive, negative)? if not you cant use a classification algorithm like Naive Bayes or SVM $\endgroup$ Jun 21 at 13:57

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