jokame / kaggle---bag-of-words-meets-bag-of-popcorns-using-word2vec-in-r

An entry to Bag of words meets bag of popcorns using word2vec in R

Kaggle Bag of Words Meets Bag of Popcorns using Word2vec in R

An entry to Bag of words meets bag of popcorns using word2vec in R

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####Packages needed:

• rword2vec
• Rcpp and RcppArmadillo
• rpart and randomForest
• tm

####Code Explanation:

• Word vectors are obtained by using rword2vec package.
• Binary output file is converted into text file for further processing.
• To create training dataset for sentiment classification for reviews using word vectors obtained above, two popular methods can be used:

1. Vector Averaging
2. Clustering

• In first methods, we have to do vector averaging for each row of labeled and test dataset. There are many ways to do this but I have done this part using Rcpp and RcppArmadillo (R interface to C++) to avoid these compute intensive operations.
• In clustering,we are doing bag of centroids instead of bag of words. This part is also done using Rcpp and RcppArmadillo to optimize speed.
• Finally, classsification is done using random forest.

####Note: I'd recommend to read this python tutorial series first for better understanding of vector averaging and clustering.

####Test dataset results:

Classification using Vector Averaging

Classification using Clustering

####Results:

• Accuracy obtained for averaging and bag of centroids is more than their respective threshold but it is still very less.
• Accuracy can be improved using different machine learning algorithms like GBM,xgboost,neural networks etc and using techniques like stacking, blending, bagging etc.