From Review to Rating: Exploring Dependency Measures for Text Classification
This work addresses computational efficiency for researchers or practitioners using text classification on datasets like student reviews, but it is incremental as it builds on existing feature selection methods.
The paper tackled the problem of high computational complexity in text classification using word vectors by applying a non-linear dependency measure for feature selection, achieving comparable accuracy to full feature vectors while being an order of magnitude faster in testing.
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.