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

TL;DR

This paper introduces a new ensemble learning method called Data Shared AdaBag Lasso, designed for low-dimensional feature extraction in word-based sentiment analysis, demonstrating its application on IMDb movie review data.

Contribution

The paper presents a novel ensemble method that enhances feature selection in sentiment analysis by combining Data Shared Lasso with bootstrap aggregation, improving upon existing algorithms.

Findings

Effective dimension reduction in IMDb data groups

Slightly higher error rate compared to state-of-the-art methods

Demonstrates the method's applicability to sentiment prediction

Abstract

In this article we propose a new supervised ensemble learning method called Data Shared Adaptive Bootstrap Aggregated (AdaBag) Lasso for capturing low dimensional useful features for word based sentiment analysis and mining problems. The literature on ensemble methods is very rich in both statistics and machine learning. The algorithm is a substantial upgrade of the Data Shared Lasso uplift algorithm. The most significant conceptual addition to the existing literature lies in the final selection of bag of predictors through a special bootstrap aggregation scheme. We apply the algorithm to one simulated data and perform dimension reduction in grouped IMDb data (drama, comedy and horror) to extract reduced set of word features for predicting sentiment ratings of movie reviews demonstrating different aspects. We also compare the performance of the present method with the classical Principal Components with associated Linear Discrimination (PCA-LD) as baseline. There are few limitations in the algorithm. Firstly, the algorithm workflow does not incorporate online sequential data acquisition and it does not use sentence based models which are common in ANN algorithms . Our results produce slightly higher error rate compare to the reported state-of-the-art as a consequence.