The challenge is to obtain Ten-fold cross validation auc score more than 0.803. The approach i have taken is to first clean the tweets, spelling correction, lemmatization, stop words removal, creating document term matrix (since all frequent words already have been removed) , dimensionality reduction and then finally fitting ML Algorithm. These approaches are pretty naive. With this approach i could reach to 0.775 10-fold cross validation auc score.
sawankumar94/Sentiment-Analysis-of-Twitter-Data-using-DTM-SVD-and-ML
The challenge is to obtain Ten-fold cross validation auc score more than 0.803. The approach i have taken is to first clean the tweets, spelling correction, lemmatization, stop words removal, creating document term matrix (since all frequent words already have been removed) , dimensionality reduction and then finally fitting ML Algorithm. These approaches are pretty naive. With this approach i could reach to 0.775 10-fold cross validation auc score.
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