Unsupervised Learning for Lexicon-Based Classification

TL;DR

This paper provides a probabilistic foundation for lexicon-based classification, demonstrating how to learn word weights from unlabeled data to improve accuracy over traditional heuristics.

Contribution

It introduces a method to derive word weights from co-occurrence statistics without labeled data, enhancing lexicon-based classification performance.

Findings

Learned word weights improve classification accuracy.

Probabilistic justification for lexicon heuristics.

Outperforms traditional word-counting methods.

Abstract

In lexicon-based classification, documents are assigned labels by comparing the number of words that appear from two opposed lexicons, such as positive and negative sentiment. Creating such words lists is often easier than labeling instances, and they can be debugged by non-experts if classification performance is unsatisfactory. However, there is little analysis or justification of this classification heuristic. This paper describes a set of assumptions that can be used to derive a probabilistic justification for lexicon-based classification, as well as an analysis of its expected accuracy. One key assumption behind lexicon-based classification is that all words in each lexicon are equally predictive. This is rarely true in practice, which is why lexicon-based approaches are usually outperformed by supervised classifiers that learn distinct weights on each word from labeled instances. This paper shows that it is possible to learn such weights without labeled data, by leveraging co-occurrence statistics across the lexicons. This offers the best of both worlds: light supervision in the form of lexicons, and data-driven classification with higher accuracy than traditional word-counting heuristics.