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In this article we propose a new supervised ensemble learning method calledData Shared Adaptive Bootstrap Aggregated (AdaBag) Lasso for capturing lowdimensional useful features for word based sentiment analysis and miningproblems. The literature on ensemble methods is very rich in both statisticsand machine learning. The algorithm is a substantial upgrade of the Data SharedLasso uplift algorithm. The most significant conceptual addition to theexisting literature lies in the final selection of bag of predictors through aspecial bootstrap aggregation scheme. We apply the algorithm to one simulateddata and perform dimension reduction in grouped IMDb data (drama, comedy andhorror) to extract reduced set of word features for predicting sentimentratings of movie reviews demonstrating different aspects. We also compare theperformance of the present method with the classical Principal Components withassociated Linear Discrimination (PCA-LD) as baseline. There are fewlimitations in the algorithm. Firstly, the algorithm workflow does notincorporate online sequential data acquisition and it does not use sentencebased models which are common in ANN algorithms . Our results produce slightlyhigher error rate compare to the reported state-of-the-art as a consequence.

Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data