Sentiment Classification with ML

Hi, I’m currently working with sentiment analysis of twitter data. So far I have managed to collect tweets from a specific search and been able to pre-process. The step I’m not understanding is the labeling process. I want my tweets to be categorized into positive, negative and neutral sentiments by using SVM, Decision Tree and Naive Bias ML methods. I have gone through several examples regarding text processing, but when it comes to labeling it stops entirely for me. How do I proceed with this step? Can I for example train a model with different dataset and then apply the trained model to my own dataset to make classification? I see that some of the examples are using category to class when using ML methods, does this node have something to with labeling?


Hello @Huseyin,

seems like a same question from another topic of yours already addressed by @ScottF. Please don’t open multiple topics for same question/issue. Closing this one.


Duplicate: Sentiment Analysis of Afghanistan Crisis using Twitter - #15 by Huseyin