I am trying to predict tags for stackoverflow questions and I am not able to decide which Machine Learning algorithm will be a correct approach for this.Input: As a dataset I have mined stackoverflow questions, I have tokenized the data set and removed stopwords and punctuation from this data.Things i have tried:TF-IDFTrained Naive Bayes on the dataset and then gave user defined input to predict tags, but its not working correctlyLinear SVMWhich ML algorithm I should use Supervised or Unsupervised?If possible please, suggest a correct ML approach from the scratch.PS: I have the list of all tags present on StackOverflow so, will this help in anyway?Thanks
StackOverflow Tags Predictor...Suggest an Machine Learning Approach please?
machine learning;classification
This exact problem was a kaggle competition sponsored by Facebook. The particular forum thread of interest for you is the one where many of the top competitors explained their methodology, this should provide you with more information than you were probably looking for: https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach In general, it appears that most people treated the problem as a supervised one. Their primary feature was a tf-idf, or unweighted BOW, representations of the text and they ensembled 1000s of single-tag models. Owen, the winner of the competition, noted that the title text was a more powerful feature than the content of the body of the post.