Leveraging Cognitive Features for Sentiment Analysis
This addresses sentiment analysis for user-generated text, but it is incremental as it enhances existing methods with new features.
The paper tackled sentiment analysis by augmenting traditional features with cognitive features from eye-movement patterns, resulting in improvements of up to 3.7% and 9.3% in F-score for polarity detection on two datasets.
Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.