Robust compositional polarity classification

Abstract

We describe a pattern-based system for polarity classification from texts. Our system is currently restricted to the positive, negative or neutral polarity of phrases and sentences. It analyses the input texts with the aid of a polarity lexicon that specifies the prior polarity of words. A chunker is used to determine phrases that are the basis for a compositional treatment of phrase-level polarity assignment. In our current experiments we focus on sentences that are targeted towards persons, be it the writer (I, my, me, ..), the social group including the writer (we, our, ..) or the reader (you, your, ..). We evaluate our system on a manually annotated set of sentences taken from texts from a panel group called ’I battle depression’. We present the results of comparing our system’s performance over this gold standard against a baseline system.