Leveraging Cognitive Features for Sentiment Analysis

Abhijit Mishra1, Diptesh Kanojia2, Seema Nagar3, Kuntal Dey4, Pushpak Bhattacharyya5
1Indian Institute of Technology Bombay, 2Indian Institute of Technology Bombay, Mumbai, 3IBM Research, 4IBM Research India, 5CSE Department, IIT Bombay


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.