From Review to Rating: Exploring Dependency Measures for Text Classification

TL;DR

This paper investigates using dependency measures for text classification, comparing word vector representations and feature selection techniques to improve efficiency without sacrificing accuracy in student satisfaction analysis.

Contribution

It introduces a novel application of non-linear dependency measures for feature selection in text classification, demonstrating improved computational efficiency.

Findings

Word vectors outperform frequency-based representations in accuracy.

Dependency-based feature selection reduces computation time significantly.

Approach maintains accuracy while enhancing efficiency.

Abstract

Various text analysis techniques exist, which attempt to uncover unstructured information from text. In this work, we explore using statistical dependence measures for textual classification, representing text as word vectors. Student satisfaction scores on a 3-point scale and their free text comments written about university subjects are used as the dataset. We have compared two textual representations: a frequency word representation and term frequency relationship to word vectors, and found that word vectors provide a greater accuracy. However, these word vectors have a large number of features which aggravates the burden of computational complexity. Thus, we explored using a non-linear dependency measure for feature selection by maximizing the dependence between the text reviews and corresponding scores. Our quantitative and qualitative analysis on a student satisfaction dataset shows that our approach achieves comparable accuracy to the full feature vector, while being an order of magnitude faster in testing. These text analysis and feature reduction techniques can be used for other textual data applications such as sentiment analysis.