Semi-supervised polarity lexicon induction

TLDR

Polarity of words is defined as positive or negative, with examples such as good, beautiful, wonderful versus bad, ugly, sad. The study investigates detecting word polarity. The authors model polarity detection as semi‑supervised label propagation on a graph of words linked by weighted relations, evaluated in English, French, and Hindi using WordNet or OpenOffice thesauri. Label propagation outperforms baseline and other semi‑supervised methods such as Mincuts and Randomized Mincuts.

Abstract

We present an extensive study on the problem of detecting polarity of words. We consider the polarity of a word to be either positive or negative. For example, words such as good, beautiful, and wonderful are considered as positive words; whereas words such as bad, ugly, and sad are considered negative words. We treat polarity detection as a semi-supervised label propagation problem in a graph. In the graph, each node represents a word whose polarity is to be determined. Each weighted edge encodes a relation that exists between two words. Each node (word) can have two labels: positive or negative. We study this framework in two different resource availability scenarios using WordNet and OpenOffice thesaurus when WordNet is not available. We report our results on three different languages: English, French, and Hindi. Our results indicate that label propagation improves significantly over the baseline and other semi-supervised learning methods like Mincuts and Randomized Mincuts for this task.