What's great and what's not: learning to classify the scope of negation for improved sentiment analysis

Abstract

Automatic detection of linguistic negation in free text is a critical need for many text processing applications, including sentiment analysis. This paper presents a negation detection system based on a conditional random field modeled using features from an English dependency parser. The scope of negation detection is limited to explicit rather than implied negations within single sentences. A new negation corpus is presented that was constructed for the domain of English product reviews obtained from the open web, and the proposed negation extraction system is evaluated against the reviews corpus as well as the standard BioScope negation corpus, achieving 80.0 % and 75.5 % F1 scores, respectively. The impact of accurate negation detection on a state-of-the-art sentiment analysis system is also reported. 1