Sentiment Composition of Words with Opposing Polarities

TL;DR

This paper investigates how sentiment is composed in phrases with opposing polarities, creating a dataset and evaluating methods to predict overall sentiment with high accuracy.

Contribution

It introduces a new dataset of opposing polarity phrases and evaluates various sentiment composition techniques, achieving over 80% accuracy.

Findings

Unsupervised and supervised methods can effectively predict sentiment in opposing phrases.

Incorporating constituent information improves sentiment prediction accuracy.

The best system achieves over 80% accuracy on the dataset.

Abstract

In this paper, we explore sentiment composition in phrases that have at least one positive and at least one negative word---phrases like 'happy accident' and 'best winter break'. We compiled a dataset of such opposing polarity phrases and manually annotated them with real-valued scores of sentiment association. Using this dataset, we analyze the linguistic patterns present in opposing polarity phrases. Finally, we apply several unsupervised and supervised techniques of sentiment composition to determine their efficacy on this dataset. Our best system, which incorporates information from the phrase's constituents, their parts of speech, their sentiment association scores, and their embedding vectors, obtains an accuracy of over 80% on the opposing polarity phrases.